{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "00hhdSjYI3Tz"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import json\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data0 = pd.read_json('measures_0.json')\n",
        "df0 = pd.DataFrame(data0['measures'])\n",
        "\n",
        "data1 = pd.read_json('measures_1.json')\n",
        "df1 = pd.DataFrame(data1['measures'])\n",
        "\n",
        "data2 = pd.read_json('measures_2.json')\n",
        "df2 = pd.DataFrame(data2['measures'])\n",
        "\n",
        "data3 = pd.read_json('measures_3.json')\n",
        "df3 = pd.DataFrame(data3['measures'])"
      ],
      "metadata": {
        "id": "LueUhjUraHHf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LKcptdMcUzlj",
        "outputId": "5bd69d6c-3f95-4a48-9059-9996e4b4ec6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                              measures\n",
            "0    {'metric': 'bugs', 'value': '0', 'component': ...\n",
            "1    {'metric': 'bugs', 'value': '0', 'component': ...\n",
            "2    {'metric': 'bugs', 'value': '0', 'component': ...\n",
            "3    {'metric': 'bugs', 'value': '0', 'component': ...\n",
            "4    {'metric': 'bugs', 'value': '0', 'component': ...\n",
            "..                                                 ...\n",
            "801  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "802  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "803  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "804  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "805  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "\n",
            "[806 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mo6RVb-AU473",
        "outputId": "43920e44-ec19-49ea-a5ee-ac8ec9b3780e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                              measures\n",
            "0    {'metric': 'bugs', 'value': '13', 'component':...\n",
            "1    {'metric': 'bugs', 'value': '3', 'component': ...\n",
            "2    {'metric': 'bugs', 'value': '2', 'component': ...\n",
            "3    {'metric': 'bugs', 'value': '7', 'component': ...\n",
            "4    {'metric': 'bugs', 'value': '0', 'component': ...\n",
            "..                                                 ...\n",
            "577  {'metric': 'vulnerabilities', 'value': '3', 'c...\n",
            "578  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "579  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "580  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "581  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "\n",
            "[582 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "28MkYqqVU8eJ",
        "outputId": "19768edf-a6ae-4f59-f283-f0d31f19f0d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                              measures\n",
            "0    {'metric': 'bugs', 'value': '0', 'component': ...\n",
            "1    {'metric': 'bugs', 'value': '1', 'component': ...\n",
            "2    {'metric': 'bugs', 'value': '0', 'component': ...\n",
            "3    {'metric': 'bugs', 'value': '0', 'component': ...\n",
            "4    {'metric': 'bugs', 'value': '3', 'component': ...\n",
            "..                                                 ...\n",
            "888  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "889  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "890  {'metric': 'vulnerabilities', 'value': '1', 'c...\n",
            "891  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "892  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "\n",
            "[893 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "46oLjBYHU-nc",
        "outputId": "2aa4e1d9-8798-4101-cf29-72fffbc1367d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                              measures\n",
            "0    {'metric': 'bugs', 'value': '0', 'component': ...\n",
            "1    {'metric': 'bugs', 'value': '1', 'component': ...\n",
            "2    {'metric': 'bugs', 'value': '1', 'component': ...\n",
            "3    {'metric': 'bugs', 'value': '1', 'component': ...\n",
            "4    {'metric': 'bugs', 'value': '0', 'component': ...\n",
            "..                                                 ...\n",
            "210  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "211  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "212  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "213  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "214  {'metric': 'vulnerabilities', 'value': '0', 'c...\n",
            "\n",
            "[215 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df0 = pd.json_normalize(df0['measures'])\n",
        "df1 = pd.json_normalize(df1['measures'])\n",
        "df2 = pd.json_normalize(df2['measures'])\n",
        "df3 = pd.json_normalize(df3['measures'])"
      ],
      "metadata": {
        "id": "v6QVNdlOasT2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df0 = pd.merge(df0, df1, on=['metric', 'value', 'component', 'bestValue'], how='outer')\n",
        "merged_df1 = pd.merge(merged_df0, df2, on=['metric', 'value', 'component', 'bestValue'], how='outer')\n",
        "merged_df = pd.merge(merged_df1, df3, on=['metric', 'value', 'component', 'bestValue'], how='outer')"
      ],
      "metadata": {
        "id": "_p3LFSM4XTj_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(merged_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kne55YO4W0h4",
        "outputId": "3ffbbff2-03c7-4b1e-a5f6-7e3f306bf04f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               metric value    component bestValue\n",
            "0                bugs     0    project_1      True\n",
            "1                bugs     0   project_10      True\n",
            "2                bugs     0   project_11      True\n",
            "3                bugs     0   project_12      True\n",
            "4                bugs     0   project_14      True\n",
            "...               ...   ...          ...       ...\n",
            "2491  vulnerabilities     0  project_322      True\n",
            "2492  vulnerabilities     0  project_324      True\n",
            "2493  vulnerabilities     0  project_328      True\n",
            "2494  vulnerabilities     0  project_329      True\n",
            "2495  vulnerabilities     0  project_335      True\n",
            "\n",
            "[2496 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df_1 = merged_df[merged_df['metric'] == 'duplicated_lines_density']\n",
        "merged_df_1.head(400)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "mmHJ5qzLKRvz",
        "outputId": "8a707f79-2470-472c-f3ce-f59db75b90ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        metric value    component bestValue\n",
              "230   duplicated_lines_density   0.0    project_1      True\n",
              "231   duplicated_lines_density   0.0   project_10      True\n",
              "232   duplicated_lines_density   0.0   project_11      True\n",
              "233   duplicated_lines_density   0.0   project_12      True\n",
              "234   duplicated_lines_density   0.0   project_14      True\n",
              "...                        ...   ...          ...       ...\n",
              "2355  duplicated_lines_density   0.0  project_322      True\n",
              "2356  duplicated_lines_density   0.0  project_324      True\n",
              "2357  duplicated_lines_density   0.0  project_328      True\n",
              "2358  duplicated_lines_density   0.0  project_329      True\n",
              "2359  duplicated_lines_density   0.0  project_335      True\n",
              "\n",
              "[197 rows x 4 columns]"
            ],
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>metric</th>\n",
              "      <th>value</th>\n",
              "      <th>component</th>\n",
              "      <th>bestValue</th>\n",
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              "  </thead>\n",
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              "      <th>230</th>\n",
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              "    <tr>\n",
              "      <th>2358</th>\n",
              "      <td>duplicated_lines_density</td>\n",
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              "<p>197 rows × 4 columns</p>\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ffd4c678-3eb5-4b43-8bbc-bd9483213115 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "merged_df_1",
              "summary": "{\n  \"name\": \"merged_df_1\",\n  \"rows\": 197,\n  \"fields\": [\n    {\n      \"column\": \"metric\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"duplicated_lines_density\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 61,\n        \"samples\": [\n          \"0.0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"component\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 197,\n        \"samples\": [\n          \"project_248\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bestValue\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(merged_df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSBmOucBLoNg",
        "outputId": "0f27ce55-b0db-4d1a-8564-122a463a692c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['metric', 'value', 'component', 'bestValue'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "общее количество ошибок"
      ],
      "metadata": {
        "id": "1g3ivgvsBicm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bugs_ranges = {\n",
        "    '0 bugs': 0,\n",
        "    '1-10 bugs': 0,\n",
        "    '11-100 bugs': 0,\n",
        "    'more than 100 bugs': 0\n",
        "}\n",
        "\n",
        "max_bug_count = 0\n",
        "for index, project in merged_df.iterrows():\n",
        "    if project['metric'] == 'bugs':\n",
        "        bug_count = int(project['value'])\n",
        "        if bug_count == 0:\n",
        "            bugs_ranges['0 bugs'] += 1\n",
        "        elif 1 <= bug_count <= 10:\n",
        "            bugs_ranges['1-10 bugs'] += 1\n",
        "        elif 11 <= bug_count <= 100:\n",
        "            bugs_ranges['11-100 bugs'] += 1\n",
        "        else:\n",
        "            bugs_ranges['more than 100 bugs'] += 1\n",
        "        max_bug_count = max(max_bug_count, bug_count)\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.pie(bugs_ranges.values(), labels=bugs_ranges.keys(), autopct='%1.1f%%', startangle=40)\n",
        "plt.axis('equal')\n",
        "plt.title('bug Distribution in Projects')\n",
        "plt.show()\n",
        "\n",
        "for key, value in bugs_ranges.items():\n",
        "    print(f\"{key}: {value}\")\n",
        "\n",
        "\n",
        "print(f\"Максимальное значение количества багов: {max_bug_count}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        },
        "id": "JAQ79h-4i43P",
        "outputId": "6d76b1ef-80f5-4685-8d3e-5e3772fef103"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 bugs: 98\n",
            "1-10 bugs: 68\n",
            "11-100 bugs: 19\n",
            "more than 100 bugs: 12\n",
            "Максимальное значение количества багов: 2945\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Примеры распространенных code smell:\n",
        "\n",
        "1.   Длинные функции, отвечающие за слишком много задач\n",
        "2.   Дублирование кода в разных местах\n",
        "3.   Неправильное именование переменных, функций и классов\n",
        "4.   Мертвый код, который никогда не вызывается\n",
        "5.   Классы-посредники, делегирующие всю работу другим классам"
      ],
      "metadata": {
        "id": "3aFZpufmBlyA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "code_smells_ranges = {\n",
        "    '0 ': 0,\n",
        "    '1-10': 0,\n",
        "    '11-100': 0,\n",
        "    '101-1000': 0,\n",
        "    'more than 1000':0\n",
        "}\n",
        "\n",
        "max_code_smells = 0\n",
        "for index, project in merged_df.iterrows():\n",
        "    if project['metric'] == 'code_smells':\n",
        "        smells_count = int(project['value'])\n",
        "        if smells_count == 0:\n",
        "            code_smells_ranges['0 '] += 1\n",
        "        elif 1 <= smells_count <= 10:\n",
        "            code_smells_ranges['1-10'] += 1\n",
        "        elif 11 <= smells_count <= 100:\n",
        "            code_smells_ranges['11-100'] += 1\n",
        "        elif 101 <= smells_count <= 1000:\n",
        "            code_smells_ranges['101-1000'] += 1\n",
        "        else:\n",
        "            code_smells_ranges['more than 1000'] += 1\n",
        "        max_code_smells = max(max_code_smells, smells_count)\n",
        "\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.pie(code_smells_ranges.values(), labels=code_smells_ranges.keys(), autopct='%1.1f%%', startangle=40)\n",
        "plt.axis('equal')\n",
        "plt.title('code smells Distribution in Projects')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "for key, value in code_smells_ranges.items():\n",
        "    print(f\"{key} code smells: {value}\")\n",
        "\n",
        "\n",
        "\n",
        "print(f\"Максимальное значение количества ошибок (code smells): {max_code_smells}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "id": "b2pKjaZSjDPJ",
        "outputId": "7473cbcd-17d2-4fc2-99f5-a2135b1d7328"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0  code smells: 30\n",
            "1-10 code smells: 46\n",
            "11-100 code smells: 77\n",
            "101-1000 code smells: 34\n",
            "more than 1000 code smells: 10\n",
            "Максимальное значение количества ошибок (code smells): 26674\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "сложность"
      ],
      "metadata": {
        "id": "ojPYLAUV818Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "complexity_ranges = {\n",
        "    'less than 10': 0,\n",
        "    '10-100': 0,\n",
        "    '101-500': 0,\n",
        "    '501-1000': 0,\n",
        "    'more than 1000':0\n",
        "}\n",
        "\n",
        "max_complexity = 0\n",
        "for index, project in merged_df.iterrows():\n",
        "    if project['metric'] == 'complexity':\n",
        "        complexity_count = int(project['value'])\n",
        "        if complexity_count < 10:\n",
        "            complexity_ranges['less than 10'] += 1\n",
        "        elif 10 <= complexity_count <= 100:\n",
        "            complexity_ranges['10-100'] += 1\n",
        "        elif 101 <= complexity_count <= 500:\n",
        "            complexity_ranges['101-500'] += 1\n",
        "        elif 501 <= complexity_count <= 1000:\n",
        "            complexity_ranges['501-1000'] += 1\n",
        "        else:\n",
        "            complexity_ranges['more than 1000'] += 1\n",
        "        max_complexity = max(max_complexity, complexity_count)\n",
        "\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.pie(complexity_ranges.values(), labels=complexity_ranges.keys(), autopct='%1.1f%%', startangle=40)\n",
        "plt.axis('equal')\n",
        "plt.title('complexity Distribution in Projects')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "for key, value in complexity_ranges.items():\n",
        "    print(f\"{key} : {value}\")\n",
        "\n",
        "\n",
        "\n",
        "print(f\"Максимальное значение сложности: {max_complexity}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 787
        },
        "id": "ocb9IFRRpwl_",
        "outputId": "4abc960d-358f-4333-dfeb-f6453557be51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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j6NGj4eHhgVdffbXWJf+un64SExOD//znP1i+fDmeeOIJPPnkk1i7di327t1bZy5XV1cANcvkNdOmTYNMJsOyZctw+fLlGut234rXX38dv//+O6ZMmVLrdJxrrl69Wu1jpVKJzp07Q5Ik0+9Hffkb69pyc9e89957AGBaq9rDwwN+fn41lolct25djXM1Jpu5Px8tRe/evREQEIANGzZUW6Jz+/btSEhIwLhx42762vvuuw8KhQIvvfRSjRF6SZJMnz+33XYbwsPD8c4779T4s7j+dTf787rx81Aul6Nbt24AUGNZUSKixuLIN9mEV199Fb///juGDBmChx56CJ06dUJWVhY2b96MAwcOwMvLC8888wz+97//4a677sLSpUvh4+ODzz77DMnJyfj++++bfffBrl27YvTo0dWWGgRQ5/QJDw8PrF+/HjNmzMBtt92GqVOnwt/fH2lpafj1118xYMAArF27FpWVlZg1axYiIyPxyiuvmM77yy+/YM6cOThz5oypWNyoR48eUCgUeOONN1BcXAyVSoXhw4cjICAAQNVPEsaMGYPNmzfDy8urzjJ0I71ejy+//BJA1YYqqamp+Pnnn3H69GkMGzYMH3zwQZ2vHzVqFIKCgjBgwAAEBgYiISEBa9euxbhx40xzxa/dxPrcc89h6tSpcHR0xPjx42/6fuuTnJyMe+65B2PGjMHhw4fx5Zdf4oEHHkD37t1Nx8yfPx+vv/465s+fj969e2Pfvn3V1iu/pjHZzP35aCkcHR3xxhtvYM6cORgyZAimTZtmWmowLCwMjz322E1f265dO7z88st49tlnkZKSggkTJsDd3R3JycnYsmULHnroIaxYsQJyuRzr16/H+PHj0aNHD8yZMwfBwcE4f/48zp07h99++w3A339eS5cuxejRo6FQKDB16lTMnz8fBQUFGD58OEJDQ5Gamor33nsPPXr0MM3JJyK6ZYJWWSFqdqmpqdLMmTMlf39/SaVSSREREdLixYurLc926dIladKkSZKXl5fk5OQk9enTR9q6dWu181xbIm3z5s3VHr+2ZNuNyxleWw4tLy/P9BgAafHixdKXX34pRUZGSiqVSurZs2eN5f1qWwbuWobRo0dLnp6ekpOTk9SuXTtp9uzZpqULH3vsMUmhUEgxMTHVXnf06FHJwcFBWrhwoemxG5e4kyRJ+vDDD6WIiAhJoVDUuuzgt99+KwGQHnroIamhZs2aZVpiEYDk4uIihYWFSRMnTpS+++4709J517txqcH//ve/0uDBgyVfX19JpVJJ7dq1k5588kmpuLi42uv+9a9/Sa1atZLkcnm1379rv++1wU2WGoyPj5cmTZokubu7S97e3tKSJUuqLYEnSZJUXl4uzZs3T/L09JTc3d2lyZMnS7m5uTXOWVe22v4cmvL5WNcSiNe72VKD48aNq3HsjX8eN1PX73N9ua/55ptvpJ49e0oqlUry8fGRpk+fLqWnp1c75salBq/5/vvvpYEDB0qurq6Sq6ur1LFjR2nx4sVSYmJiteMOHDggjRw5UnJ3d5dcXV2lbt26Se+9957peb1eLz366KOSv7+/JJPJTNf67rvvpFGjRkkBAQGSUqmU2rRpIz388MNSVlZWvb83RET1kUlSM9wlRkTVyGQyLF68GGvXrhUd5Zb89NNPmDBhAvbt21dtOToic3rhhRfw2muv1VjGk4jImtnmzzWJqEk+/PBDREREVFuDmsjcsrKyTDuFEhHZCs75JiKTTZs24fTp0/j111+xZs2aZl9ZhKghLl++jC1btmDz5s24++67RcchImpWLN9EZDJt2jS4ublh3rx5WLRokeg4ZKf27duHl156CUOHDsVbb70lOg4RUbPinG8iIiIiIjPhnG8iIiIiIjNh+SYiIiIiMhOWbyIiIiIiM2H5JiIiIiIyE5ZvIiIiIiIzYfkmIiIiIjITlm8iIiIiIjNh+SYiIiIiMhOWbyIiIiIiM2H5JiIiIiIyE5ZvIiIiIiIzYfkmIiIiIjITlm8iIiIiIjNh+SYiIiIiMhOWbyIiIiIiM2H5JiIiIiIyE5ZvIiIiIiIzYfkmIiIiIjITlm8iIiIiIjNh+SYiIiIiMhOWbyIiIiIiM2H5JiIiIiIyE5ZvIiIiIiIzYfkmIiIiIjITlm8iIiIiIjNh+SYiIiIiMhOWbyIiIiIiM2H5JiIiIiIyEwfRAYiIWoxBD+jKAMgAuQKQyQGZour/5QrR6YiIyA6xfBORZdKWAZpSoLIE0Pz1y/T/N3m88q/nrv2/vqKei9RSymXyql+1Pa5wBJy9ARff63753PCxL+DsU/U4Cz4REd1AJkmSJDoEEdkZoxEoyQCK0q77lfr3f0uyAKNOdMomkgFOnjWL+bWy7hYI+IQDvu0BVz/RYYmIyExYvomo+RmNQGnWDcU69e+PizNsoFw3IydPwKcd4Nuuqoyb/r9d1XNERGQzWL6J6NZVFAE5Z4Hss0BufFXBLkytGtU2aEWnsw2u/n+V8faAb0T1cu7oLDodERE1Ess3EdVPkoCCy0D2mb/Lds5ZoPiK6GR2TAZ4hFSVcP+OQKveQGjvqo+JiMhisXwTUXXXinbGcSDzeNV/c84CWrXoZNQQzj5Aq15VRTy0d9X/O3uLTkVERH9h+SaydyWZVQU741hV2c48AVQWi05FzUZWNVXFVMZ7A4FdAQUXuyIiEoHlm8jelGQBl/dU/UreB5Rmik5E5ubgDIT0+GuE/PaqUu4ZKjoVEZFdYPkmsnUaNZByALi8u6pw550XnYgskXtwVRkPHwxEjqpaBpGIiJodyzeRrTHoq6aQXCvb6Ue5rB81nm/7qhIeORJoOxBwUIpORERkE1i+iWxBXuLfU0lSDlTt8EjUXJRuf42Ij6wq5JyiQkR0y1i+iayROreqaF/aDSTvrVpXm8hcAjoD7UdUFfE2/XjzJhFRI7B8E1mLkkzg3Bbg7A9AxlHRaYiqqDyBiCF/T1FxDxKdiIjIorF8E1kydS4Q/xNw9nsg7QgA/nUlSyYDgqKriniHsUBoL9GBiIgsDss3kaUpLwASfq4a4U45AEgG0YmIbo13GNB1EtBtMuDfQXQaIiKLwPJNZAkqS4Dzv1aNcF/ew9VJyPYERgPRk6p+8YZNIrJjLN9EomjLgMTtVfO4L+4EDBrRiYjMQFZ1k2b0JBi6TITCxUt0ICIis2L5JjInXSWQtLNqhPvCb4CuXHQiImEWerwPyb8TJvUKxdAO/nBQyEVHIiJqcSzfROaQfQaI/bBqlJtrcBOhwrcrOmX80/Sxv7sKE3qE4P7erREV6C4wGRFRy2L5JmopBl3VSiWxHwJXjohOQ2RRfgtdhoeT+tb6XHQrT9zfOxT3dm8FTxdHMycjImpZLN9Eza0kEzj6KXD8M0CdIzoNkcWRZAqMln+AC2XOdR6ndJBjXHQw5g0MR9dWnmZKR0TUsli+iZpL8n4g7sOqVUuMetFpiCzW1eAh6JX8cKNe0yfcB3MHhGNU50DI5bIWSma9hg4dih49euCdd94RHQUAsGfPHgwbNgyFhYXw8vISHYfIovDuFqKm0KirppW8fwfw2d1V00xYvInq9AsGN/o1sckFeOTLYxj6nz345EAy1Br+PbMUQ4cOxfLly0XHMNm3bx/Gjx+PkJAQyGQy/PjjjzWOkSQJK1euRHBwMJydnTFixAhcvHjR/GHJLrF8E92KvAvAtieBtzoB21YAeQmiExFZBUnphrfTI2/59WkF5fi/rfHo9+ou/GtrPK4UcMUgqq6srAzdu3fH+++/f9Nj3nzzTbz77rvYsGEDYmJi4OrqitGjR6OystKMSclesXwTNZTRACT8Anw2Hnj/diD2A65cQtRIKQEjUKxzaPJ5SjV6fHwgGUP/swePfHEMcSkFzZDOdmg0GqxYsQKtWrWCq6sr+vbtiz179pieT01Nxfjx4+Ht7Q1XV1d06dIF27ZtAwAUFhZi+vTp8Pf3h7OzMyIjI/Hpp5/Wep3Zs2dj7969WLNmDWQyGWQyGVJSUkzPHzt2DL1794aLiwv69++PxMRE03OXLl3Cvffei8DAQLi5ueH222/HH3/8Ue38YWFhePXVVzF37ly4u7ujTZs2+OCDD+p873fddRdefvll/OMf/6j1eUmS8M477+D555/Hvffei27duuHzzz9HZmZmraPkRM2N5ZuoPpXFwP7VwDvdgG8eBJL3iU5EZLW+rOjXrOczGCXsOJeN+zccxj1rD+CnkxnQGYzNeg1rtGTJEhw+fBibNm3C6dOncf/992PMmDGmqRWLFy+GRqPBvn37cObMGbzxxhtwc3MDALzwwguIj4/H9u3bkZCQgPXr18PPz6/W66xZswb9+vXDggULkJWVhaysLLRu3dr0/HPPPYfVq1fj6NGjcHBwwNy5c03PqdVqjB07Frt27cKJEycwZswYjB8/HmlpadWusXr1avTu3RsnTpzAokWLsHDhwmolvrGSk5ORnZ2NESNGmB7z9PRE3759cfjw4Vs+L1FDNX34gchWVZYAR9YDR9YBlUWi0xBZPb17K3yS2XJby59OL8ayTSfx2rbzmNGvLab3bQMvF2WLXc9SpaWl4dNPP0VaWhpCQkIAACtWrMCOHTvw6aef4tVXX0VaWhomTpyI6OhoAEBERES11/fs2RO9e/cGUDX6fDOenp5QKpVwcXFBUFBQjedfeeUVDBkyBADwzDPPYNy4caisrISTkxO6d++O7t27m47917/+hS1btuDnn3/GkiVLTI+PHTsWixYtAgA8/fTTePvtt7F792506NDhln5/srOzAQCBgYHVHg8MDDQ9R9SSWL6JbsTSTdQiTnuPhJTX8iuVZJdU4t+/JWLtn0n4x22tsHBIO7T2cWnx61qKM2fOwGAwICoqqtrjGo0Gvr6+AIClS5di4cKF+P333zFixAhMnDgR3bp1AwAsXLgQEydOxPHjxzFq1ChMmDAB/fv3v6Us184JAMHBwQCA3NxctGnTBmq1GqtWrcKvv/6KrKws6PV6VFRU1Bj5vv4cMpkMQUFByM3NvaU8RJaA006IrqksAfa+CbwTDex5lcWbqJm9f7W3Wa9XoTPg65g03Ll6L1748SxyS+zjZjq1Wg2FQoFjx47h5MmTpl8JCQlYs2YNAGD+/Pm4fPkyZsyYgTNnzqB379547733AFTNmU5NTcVjjz2GzMxM3HnnnVixYsUtZXF0/HuTJJms6hsvo7FqWtCKFSuwZcsWvPrqq9i/fz9OnjyJ6OhoaLXam57j2nmuneNWXBuhz8mpvg9DTk5OraP3RM2N5Zvo+tK9+xWWbqIWUOHbFbuu+gi5ttZgxBdHUjH437vx6rYEFJZp63+RFevZsycMBgNyc3PRvn37ar+uL5etW7fGI488gh9++AFPPPEEPvzwQ9Nz/v7+mDVrFr788ku88847dd7kqFQqYTAYGp3z4MGDmD17Nv7xj38gOjoaQUFB1W7WbCnh4eEICgrCrl27TI+VlJQgJiYG/fo17z0JRLXhtBOyX5pS4MgG4Mj7QEWh6DRENm2v852iI6BSZ8QH+y7j65g0zB0YjgWDwuHuZHvb10dFRWH69OmYOXMmVq9ejZ49eyIvLw+7du1Ct27dMG7cOCxfvhx33XUXoqKiUFhYiN27d6NTp04AgJUrV6JXr17o0qULNBoNtm7danquNmFhYYiJiUFKSgrc3Nzg49Owb7IiIyPxww8/YPz48ZDJZHjhhReaNKJ9jVqtRlJSkunj5ORknDx5Ej4+PmjTpg1kMhmWL1+Ol19+GZGRkQgPD8cLL7yAkJAQTJgwocnXJ6oPyzfZH5ZuIrOS5A5YndWt/gPNRK3R491dF/H54RQ8MqQdZvcPg5OjQnSsZvXpp5/i5ZdfxhNPPIGMjAz4+fnhjjvuwN133w0AMBgMWLx4MdLT0+Hh4YExY8bg7bffBlA1kv3ss88iJSUFzs7OGDRoEDZt2nTTa61YsQKzZs1C586dUVFRgeTk5AZlfOuttzB37lz0798ffn5+ePrpp1FS0vTlW48ePYphw4aZPn788ccBALNmzcLGjRsBAE899RTKysrw0EMPoaioCAMHDsSOHTvg5OTU5OsT1Yfby5P90JQCMRuAwyzdROZ0K9vJm1OAuwpLhrfH1NvbQOnA2ZhE1LJYvsn2sXQTCfVp8At4Kfnm0xYsRSsvZywbEYmJt4VCIW/5VVmIyD6xfJPtMhqBY59W3URZflV0GiK7JKnc0aP8/WbZ1dJcIvxd8fjIKIyLDjat0EFE1FxYvsk2pR0Btj0JZJ8WnYTIriWHTsCwpMmiY9ySzsEeeGJUFO7sFFj/wUREDcTyTbalJAvYuRI4863oJEQE4P9838AnGa3rP9CC9Q33wb8mdEVUoLvoKERkA1i+yTbotVU7Uu77N6BVi05DRKjaTj4y/01IkvVP3XBUyDB3QDiWjYiEi9J6ptAQkeVh+Sbrd3EnsOMZ4GpS/ccSkdkcaz0HEy+OFB2jWQV7OuGFuztjbHSw6ChEZKVYvsl6FVwGdvwTuLBddBIiqsVc1/fx51Vv0TFaxOAof/zfPV0Q5ucqOgoRWRmWb7I+2jJg/2rg0FrAoBGdhohqUe4Xjc7pz4qO0aKUDnI8MjgCi4a1t7lNeoio5XA3AbIuZ74D1t5eVb5ZvIks1l6n4aIjtDit3oh3/0zCqLf34c/zOaLjEJGV4Mg3WYfss8D2p4DUg6KTEFE9JLkDRuK/SCp3Fh3FrEZ2DsSL4zsj1NtFdBQismAs32TZKoqAP18Gjn4CSAbRaYioAfJDhqL35YdExxDC2VGBJcPbY8GgCG5VT0S14lcGslxJu4D1/YG4D1m8iazIz9Ig0RGEqdAZ8O/fEnHXmn04lJQvOg4RWSCOfJPl0ZYBv78AHP1YdBIiaiRJ5Y5uZe+jVM+1sAFgfPcQrLy7M/zdVaKjEJGF4Mg3gH379mH8+PEICQmBTCbDjz/+WO15SZKwcuVKBAcHw9nZGSNGjMDFixfrPe/SpUvRq1cvqFQq9OjRo9ZjTp8+jUGDBsHJyQmtW7fGm2++WeOYzZs3o2PHjnByckJ0dDS2bdt2K2/TOqQdATYMZPEmslLJ/iNYvK/zy6lMjH5nH7afyRIdhYgsBMs3gLKyMnTv3h3vv/9+rc+/+eabePfdd7FhwwbExMTA1dUVo0ePRmVlZb3nnjt3LqZMmVLrcyUlJRg1ahTatm2LY8eO4d///jdWrVqFDz74wHTMoUOHMG3aNMybNw8nTpzAhAkTMGHCBJw9e/bW3qyl0muqtoX/9K6q9buJyCp9Xt5PdASLU1CmxcKvjmP5phMortCJjkNEgnHayQ1kMhm2bNmCCRMmAKga9Q4JCcETTzyBFStWAACKi4sRGBiIjRs3YurUqfWec9WqVfjxxx9x8uTJao+vX78ezz33HLKzs6FUKgEAzzzzDH788UecP38eADBlyhSUlZVh69atptfdcccd6NGjBzZs2NAM79gCZJ0GtjwM5MaLTkJETaB3D0Vk/hs2sZ18SwnycMKbk7phcJS/6ChEJAhHvuuRnJyM7OxsjBgxwvSYp6cn+vbti8OHDzfp3IcPH8bgwYNNxRsARo8ejcTERBQWFpqOuf7a145p6rUtgtEA7P038OFwFm8iG3DSexSLdz2ySyox85NYPLflDMq1etFxiEgAlu96ZGdnAwACAwOrPR4YGGh6rinnru2811/3Zsc09drC5V8EPh4J7H4ZMPLHsES2YG1+L9ERrMZXMWm4a81+HE0pEB2FiMyM5bsZ3HXXXXBzc4Obmxu6dOkiOo5lkyTgyHpgwyAg45joNETUTMr9umFPgbfoGFYl9Wo5pnxwBKt/T4TeYBQdh4jMhLek1yMoKAgAkJOTg+DgYNPjOTk5phVMPvroI1RUVAAAHB0dG3XunJzqWxJf+/jadW92zLXnrUpRGvDjIiBlv+gkRNTM7GE7+ZZgMEp4788kHEjKx7tTe6K1D3fHJLJ1HPmuR3h4OIKCgrBr1y7TYyUlJYiJiUG/flV39bdq1Qrt27dH+/bt0bZt2wafu1+/fti3bx90ur+nXezcuRMdOnSAt7e36Zjrr33tmGvXthrHvwDWD2DxJrJBktwB/8mMFh3Dqp1IK8LYNfux5US66ChE1MJYvgGo1WqcPHnStBpJcnIyTp48ibS0NMhkMixfvhwvv/wyfv75Z5w5cwYzZ85ESEiIaUWUm0lKSsLJkyeRnZ2NiooK0zW0Wi0A4IEHHoBSqcS8efNw7tw5fPPNN1izZg0ef/xx0zmWLVuGHTt2YPXq1Th//jxWrVqFo0ePYsmSJS3129G8yguAr6cCPy8BNCWi0xBRC8gPGoRL5c6iY1i9Uo0ej31zCss2nUBpJe+FIbJVXGoQwJ49ezBs2LAaj8+aNQsbN26EJEl48cUX8cEHH6CoqAgDBw7EunXrEBUVVed5hw4dir1799Z4PDk5GWFhYQCqNtlZvHgx4uLi4Ofnh0cffRRPP/10teM3b96M559/HikpKYiMjMSbb76JsWPH3vobNpesU8A3D1ZNNyEim/Vx8Er8K7mj6Bg2pbWPM96Z0hO92nIePZGtYfmmlnHya2DrY4C+/o2IiMh6SSoPdCtby10tW4BCLsOTozvgkSHtREchombEaSfUvAw6YOvjwI8LWbyJ7EByALeTbykGo4TXt5/Hkq+Po0JrEB2HiJoJyzc1n5IsYOM44OjHopMQkZl8VnaH6Ag2b+vpLNy3/hCuFJSLjkJEzYDTTqh5pB4CNs8G1Dn1HkpEtoHbyZuXt4sj1j5wGwa09xMdhYiagCPf1HRH1gOfjWfxJrIzJ7y4nbw5FZbrMPOTWHy477LoKETUBBz5plunLQd+WQac+VZ0EiISYJbr+9h7latxiHBvjxC8MbEbnBwVoqMQUSOxfNOtKUgGvpkB5JwRnYSIBCj3647O6U/XfyC1mC4hHvjvjF4I9eaumETWhNNOqPEu7gQ+GMriTWTH9jjV3BuBzOtcZgnuWXsQhy7li45CRI3AkW9qOEkC9r4J7H0dkIyi0xCRIJLcEXfiv7hc7iQ6CgFwkMvw7NhOmDcwXHQUImoAlm9qmMpi4IeHgQvbRSchIsHyQobj9svzRcegG9zXsxVevS+a88CJLBynnVD9rl4CPhjG4k1EAICfjINER6Ba/HAiA/dvOIzMogrRUYioDhz5prpdiQP+NwUovyo6CRFZAG4nb/n83JR4/4Hb0DfCV3QUIqoFR77p5s7/WrV+N4s3Ef3lcsBIFm8Ll6/WYsYnsdh+Jkt0FCKqBcs31S72w6qlBPX88SUR/e1zbidvFbR6IxZ/fRxfx6SJjkJEN+C0E6pOkoA/VgEH3xGdhIgsjN6jNSLzXueullZmxagoLBkeKToGEf2FI9/0N70W+OEhFm8iqtVxT24nb43+8/sFvPTLOXCsjcgysHxTlcoS4KuJ3CqeiG5q7dVeoiPQLfr0YAoe++YkdAbu0UAkGss3AWX5wGd3A8n7RCchIgtV5t8D+wq8RMegJvjxZCYWfH4UFVqD6ChEdo3l294VXQE+GQ1knRKdhIgs2B4Vt5O3BXsS8zD9oyMoLteJjkJkt1i+7VleYlXxvpokOgkRWTBJ7ojVmdGiY1AzOZ5WhPv/ewjZxZWioxDZJZZve5VxDPhkDFCSIToJEVm4vKDBuFzuJDoGNaMLOWpMXH8Il/PUoqMQ2R2Wb3t0eS/w2T1ARYHoJERkBX4yDhQdgVpARlEF7t9wGGfSi0VHIbIrXOfb3iT8Anw3DzBoRCchIisgqTzRtWwtyvQK0VGohbipHPDfGb0woL2f6ChEdoEj3/bk9Gbg21ks3kTUYJcCRrB42zi1Ro85G+OwjdvRE5kFy7e9iP8J2PIwIHGJKSJquM+4nbxd0OqNWPL1cfxwPF10FCKbx/JtDxJ3VE01YfEmokbQe7TBl1khomOQmRgl4MnvTnMEnKiFsXzbukt/At/OBIxc05WIGucYt5O3OwajhGWbTuDP8zmioxDZLJZvW5ZyENg0nXO8ieiWvMft5O2SziDhkS+P42BSvugoRDaJ5dtWXYkDvp4C6MpFJyEiK1Tm3wMHCjxFxyBBtHojFnx+FEdTuCQtUXNj+bZFWaeAryYC2lLRSYjISu1WDRcdgQQr1xow59M4nE4vEh2FyKawfNuanHjg8wlAJTdNIKJbI8kd8Z+MrqJjkAUo1egx85NYnM8uER2FyGawfNuS/IvA5/dy50oiapK8oMFIqeB28lSlqFyHBz+KxSVuRU/ULFi+bUVBctWW8WW5opMQkZX70ThIdASyMPlqDR78KAZXCngfEVFTsXzbguL0quJdmik6CRFZOUnliTXp7UTHIAuUVVyJBz46guziStFRiKway7e1K80GPhsPFKeJTkJENiApYCS3k6ebulJQgQc+OoJ8NZewJbpVLN/WrCy/ao53wWXRSYjIRnxe1ld0BLJwl/PK8OBHMSgq14qOQmSVWL6tVUVh1aomeedFJyEiG6HzaMvt5KlBzmeXYuYnsSit5O7JRI3F8m2N9Brg66lAzhnRSYjIhhzndvLUCKfTizF3YxwqdQbRUYisCsu3Nfp5KXDliOgURGRj3s2/TXQEsjJxKYV44ttTkCRJdBQiq8HybW32rwZObxKdgohsjNq/Jw4Wcjt5arxfz2Thzd8SRccgshos39Yk/mdg179EpyAiG8Tt5Kkp1u+5hG/iuOoWUUOwfFuLzBPAlocB8Ed7RNS8JIUSqzO6iI5BVu65LWdxMClfdAwii8fybQ1KsoD/TQN03FmMiJpfbiC3k6em0xslPPLlMVzMKRUdhciisXxbOm058L+pQGmW6CREZKO2GAaKjkA2orRSjzkb47gJD1EdWL4tmSRVTTXJOik6CRHZKKOTF95Lby86BtmQ9MIKvLn5LPRcgpCoVizfluzPfwEJP4tOQUQ27JL/SJQZ+E8BNZ+7A7zQ/pQaf37OTeCIasOvuJbq1KaqZQWJiFrQRjW3k6fm83CIPzonaaGrNOBiXA6O7UgRHYnI4rB8W6K0I8DPj4pOQUQ2TufRFl9xO3lqBo4yGf4ZGAiPeDUk49+rcsX8dBnJp7kCCtH1WL4tTWEKsGk6YNCKTkJENu6Y5yjREcgG+Ckd8LyHH3SJJTWekyRg5yfncDVDLSAZkWVi+bYklSXA11OBco4SEFHLe4/byVMTdfFwxhKjO9SpNy/XukoDtq0/jUq1zozJiCwXy7elMBqA7+YAeQmikxCRHVD738bt5KlJRvl5YkK+A8ryK+s9tiS/Ejs/PQdJ4kZxRCzfluK354CkP0SnICI78adqmOgIZMXmBvujZ7IO2nJ9g1+Tdq4Ax7antmAqIuvA8m0J4n8GYtaLTkFEdkJSKPEfbidPt0AO4OmgQPgmqGE0NH4UO3ZrMtITC5s/GJEVYfkWregKVzYhIrPKDRyMNG4nT43k6aDASu8AGM/XvLGyoSSjhN8/PoeyYu6ASfaL5Vskgx74fh5QWSQ6CRHZkR8Mg0RHICsT6eaMxxWeKEsubfK5Kkq0+P2jczAaOf+b7BPLt0h7XgOuxIhOQUR2xOjkjbXp7UTHICsy1NcDU4scoc6paLZzZl4sQsxPl5rtfETWhOVblOR9wIG3RKcgIjuT5D+C28lTgz0Y7Ic7UvUtskzg8d/TuAEP2SV+BRahLB/4fgEgGUUnISI7s1F9h+gIZAVkAFYEByI4oQwGfQtND5GAXRvjUZLffCPqRNaA5dvcJAn4cSGgzhadhIjsjM4zDF9nBYuOQRbO3UGBlb4BkCXc+o2VDaUp1+O3D8/CoOdgFNkPlm9zO7IOuPi76BREZIeOenA7eapbmIsKKxy9UH6p6TdWNlRuaikObr5otusRicbybU6ZJ4A/VolOQUR26t38nqIjkAXr7+OOmWonqLPKzX7tM3szcPlEntmvSyQCy7e5aEqB7+YCBq3oJERkh9QBvXCY28nTTUwJ8sWQdCMqSsT9G7X7q/MoF3h9InNh+TaXX58ACi6LTkFEdmqXI7eTp9otCwlAm8Ry6LVi511XqnXY/UWC0AxE5sDybQ4nvwZOfyM6BRHZKUmhxOrMzqJjkIVxVsix0j8AyvhSwEL2u0k5cxXxBzJFxyBqUSzfLS0/Cfh1hegURGTHcgKHcDt5qibUWYlnnHxQcdF8N1Y21IHvLnL5QbJpLN8tSa8FvpsD6MpEJyEiO/aDYaDoCGRBenu5YW6FM9QZlvlvk67SgD82xkPi9vNko1i+W9LOF4Ds06JTEJEdMzp5431uJ09/mRDog1FZQEWRZd/YmJVUjBM700THIGoRLN8tJXkfELNBdAoisnMX/UdyO3kCACwKCUDUhQroNAbRURok5pfLyE9Xi45B1Oz4Fbkl6CqBX5aLTkFEhE/VfUVHIMFUchmeDwiEa3wpJCuayWHUS/jj03jufkk2h+W7Jex7Eyi4JDoFEdk5nWc4NnE7ebsW6OSIf7r5QnOh5beKbwlXM9SI+ZnL9JJtYflubjnxwMF3RacgIkKcx0jREUigbp4ueETnCnWaZd5Y2VAnd6YhM6lIdAyiZsPy3ZyMRuCXpYBRJzoJERHW5N0mOgIJMi7AG+NzFSi/qhEdpckkCfjz8wQYdJx+QraB5bs5Hf0YSI8TnYKICKUBvRFT5CE6BgnwUIg/uiZpoK3Qi47SbIpzK3B0e4roGETNguW7uZRkArv+T3QKIiIAwC7lUNERyMwcZDI8GxgIz3g1jDa4Rvbx31NRmG3dU2iIAJbv5rPtSUBjnTe0EJFtkRQq/Ce9i+gYZEY+jg54wdMP+kTb/XfIqJew56tE0TGImozluzkk/AKc3yo6BRERACAncDDSK1WiY5CZdHJ3xlK4Q51i+2tiZ14sQsKhTNExiJqE5bupKkuAbU+JTkFEZPKdYZDoCGQmI/08MbHAAWV5laKjmM2h7y+hQm3ZO3QS1YXlu6l2vQSU8rtwIrIMRmcfvJ8eLjoGmcHsYD/clqKDpsx2bqxsiMoyHQ59lyQ6BtEtY/luiiuxwNFPRKcgIjK56DcSFQaF6BjUguQAngoOhH9CGYx627uxsiHOH8lGRmKh6BhEt4Tl+1YZdMAvywCJ644SkeX4hNvJ2zQPRwVWegdASrDdGysbas/XiVz7m6wSy/etOvgOkBsvOgURkYnOMwLfZAWJjkEtpL2rE55QeKIsuVR0FItQlFOOY7+lio5B1Ggs37fi6iVg339EpyAiqibOY4ToCNRCBvt6YFqJEursCtFRLMrxHakoyikXHYOoURxEB7BKvywD9PZzZzkRWT4JMrxj5u3kJaMBxQe+hjp+D4xlhVC4+cC1653w7D8VMpms1tdUpp9D0Z6N0F1Nh6TXQOERAPceY+Bx+wTTMepzu1G09zNI2gq4Ro+Az50LTM/pi3OQ880LCJ71DuQql5Z+ixbhgWA/tEmqQCWnWNRg0Bux75sLuGdpD9FRiBqM5buxznwHpOwXnYKIqBp1QC/Eppl3O/mSmO9RenI7fMc9BqVfG2iyLuLq9jWQq1zh0fueWl8jd3SC+213w9E/DHKlEyrT41Hw21rIHJ3g3mMMDOXFKNjxHnzHLoeDVxByv3sJTm27w6V9HwDA1d/XwXvIbLso3jIAjwUHQJFQCoPoMBbsSnwBUs9eRduuvqKjEDUIy3dj6LXAn/8SnYKIqIadjsPMfk1NRgKc2/eFS7vbAQAOnoEoT9gHbdaFm75GGdgOysB2po/dPANRfuEQNOnn4N5jDPRF2ZCpXODaaTAAwKlNN+iuXgHa90FZ/F7I5A5w6dC/Zd+YBXB1kGOFlx/KEzi/uyEOfp+E1p19IJfX/hMXIkvCOd+NcfQToDBFdAoiomokhQqrMzqb/bqqVp1QmXoKuoIMAIA29zIq0+PhFNGrwefQ5lyCJiMBqtZdAQAOPq0g6TTQ5lyCoaIU2qwLUPqHwVCpRtH+L+Ez8pEWeS+WpI2LCk8pvVGexOLdUIVZZYg/wD03yDrIJEmyz0VCG0tTCqzpAZTni05CRFRNdqtRuOPSbLNfV5KMKNr7OUpivgfkcsBohNfgGfDsN7ne16a/PwuGimLAaITngGnwGjDN9Fz5hUMo2v8VJL0Wrl2GwmvgdORvWwOlfxiUQe1Q8McHgFEPzwEPwLXjwJZ8i2Z3h7c7RhXIUVHMHRwby9ndEQ/+Xz8onflDfbJs/AxtqINrWLyJyCJ9pxNTQMsT9qMsfg/8xq+Ao39baHMuo3DXh1C4+cIt+s46Xxs4/Q1IukpoMs6jaO9ncPQOgWvnIQAAl6j+cIn6e2pJZdoZ6PJS4DPyYWR+8BD8xj8Jhas3sj5/HE6tu0Lh6tWSb9NsJgX6IDJZgwqtfe1Y2VwqSnU4tiMV/f7Rrv6DiQRi+W6I0hzg8PuiUxAR1WB09sX7GWK2ky/c8yk875hkKs1K/zDoS3JRfGRzveXb0SvI9BpDeRGKDn5tOs/1JL0OBb+vh+/dj0NfmAXJaIBTm+iqc/i0giYrES7trX9joaUhAVAllMJON6xsNqf+vIIug0Pg4essOgrRTXHOd0PseQ3QcR1RIrI8F/xGCNtOXtJpAFn1f0ZkMnnjd/6VJEh6Xa1PFR/aBKeI26AKal91XuPf635IRj1gtO7l95zkcrzgHwhVfCnA4t1kBp0RR7ZcEh2DqE4c+a5P/kXgxBeiUxAR1erjUnGjvs7t+6D40DdQePhD6dcG2pxLKIn7EW7dRpqOKdy7EYbSq/C7+wkAQOnxrVB4+MPRJxQAUHnlHEpif4B7r5pLE2rz01B2fj+CZ78LAHDwCQVkcpSe+h0KN2/orqZDGRxphnfaMoKdlHjEwQPqi9wqvjldPJqLbncWIyjcU3QUolqxfNfnj1WAkfPviMjyaL0isDlb3HbyPiMeRtH+L1Hw+zoYy4uhcPOBW4+74DVgqukYg7oQ+pI808eSJKFo72fQF+cAMgUcvYPhPXQO3HqMqXZuSZJQsGMtvIfPh1zpBACQO6rgO3Y5Cnauh2TQwWfkI3Bw9zPPm21mvbxccXexI9TZZaKj2KSDm5Mw8amGr7pDZE5c7aQuV2KBj0fWfxwRkQAHWz+M6RdrzpMmy3ZvoDe6pOqgq+TWOS1p1PwuiOwdKDoGUQ2c812XnStFJyAiqpUEGd7ONe928tR0C0MC0OGihsXbDI78eAkGvXXfE0C2ieX7Zs5vA9IOi05BRFSr0oDeOFrsLjoGNZCjTIbnAgPhFl8KycgfOJtDSX4lEg5liY5BVAPnfNfGaAB2vSQ6BTWj9XFarD+qRUpR1ShIlwAFVg5W4q5Ix2rHSZKEsV+XY0eSAVumOGNCR8faTgcAmP1jBT47VX2FhtHtFNjxoCsAQKOXMP+XSvx0XocgNznWjXPCiIi//8r9+6AGacVGvDeWS2JR4/3hOFR0BGogf5Ujlqi8oE7kjZXmdmxHCjr1D4bCgWONZDlYvmtz8isg77zoFNSMQj1keH2ECpE+ckgAPjupw72bKnDiYTm6BPy9TNs7R7SQQdbg845pr8Cn9/5dnlWKv1/7wTEdjmUacHieK7Yn6fHA9xXIWeEGmUyG5EIjPjyuw9GHXJvl/ZF9EbWdPDVetIcLJpapoM5Ri45il9QFGiQcykLXwa1ERyEy4beCN9JVALtfE52Cmtn4Do4YG+mISF8FonwVeOVOJ7gpgSPpf8+7PJltwOrDWnxyr1ODz6tSyBDkJjf98nb+u3wn5BtwTwcHdAlQYPHtSuSVS8gvr/px88JfK/DGCBU8VA0v+kTXZAcNRUalSnQMqscYfy/ck69A2dVK0VHs2rHtKZz7TRaF5ftGR9YBpZmiU1ALMhglbDqrQ5kO6Ne6atS7XCfhge8r8P5YJwS5NfyvxZ4UPQL+XYoOa9VYuLUCV8v//gLfPVCBA2kGVOgk/HZJj2A3GfxcZPjqtA5ODjL8o9PNp7QQ1eU73QDREage80P80f2yFtpyLlUrmrpQg4SD/HedLAennVyvvAA4sEZ0CmohZ3IM6PdxGSr1gJsS2DLFGZ39q8r3Yzsq0b+1AvfWMcf7RmPaO+C+Tg4I95LjUqER/9ylwV1flePwPFco5DLM7emI0zkGdF6nhp+LDN/e74zCSmDlnkrsmeWK5/+sxKazOrTzkeOTe5zRyoPfC1P9jM6+WCdoO3mqnxzAU0GBMMSXgGOtluPYjlR0GhDCud9kEVi+rxezAdAUi05BLaSDnxwnH3FDcaWE7+J1mPVjJfbOliOpwIg/Uww48XDj5l9P7fp3UY8OVKBboALt3lVjT4oBd0Y4wFEhw/vjqt9MOeenCizto8SJbAN+PK/HqUfc8OZBDZbuqMT3k12a5X2SbUv0G4mKQjHbyVPdvB0dsNzVG+rzvLHS0lwb/e46JFR0FCKWbxNtORD7oegU1IKUChna+1TNse4VokBcpgFrjmjh7CjDpQIjvF4vrXb8xG8rMKiNFntmN6yUR3jL4eciQ1KBEXdG1Hx+d7Ie53IN+Gi8E57cqcHYSAe4KmWY3MURazeWN/n9kX0QuZ083VwHN2dM0zpDncIbKy0VR7/JUrB8X3PyK6CiQHQKMiOjBGgMwEvDlJh/W/XpJtHry/D2aBXGRzV8Gkp6iRFXyyUEu9e8ibJSL2Hxtkp8dZ8zFHIZDEbg2t6yOmPVPHSi+mi9IvBdNnfsszR3+nmiX5aEsrIK0VGoDupCDeIPZCJ6KEe/SSx++wdUret9eK3oFNSCnv2jEvtS9UgpMuJMjgHP/lGJPSkGTI92RJCbHF0DFNV+AUAbTznCvf/+K9JxrRpbEqrW9VZrJTz5eyWOpFedc9dlPe7dVI72PnKMblfze9p/7a0a6e4ZXHXuAW0U+OG8DqdzDFgbq8WANvw+mOoX6z5SdAS6wcxgP9yeooOmTFf/wSTc8d9SufIJCcd/8QEg4WegMEV0CmpBuWUSZm6pQJZagqdKhm6Bcvz2oAtG1lKUbybxqhHFmqoRaoUMOJ1rwGendCiqlBDiLsOodg741zAVVA7VR77P5hrwbbweJ6+bUz6pswP2pDhg0Kdl6OArx9cTOd+b6iZBhne4nbzFkAFYERwIJJSAG8VbD45+kyWQSZLEn3d/MAzIPC46BRHRTZUE9kG31OWiYxAAdwcFnvDwRdnl0voPJovj5q3Cgy/3g0LBH/6TGPzMSznA4k1EFm+nw1DREQhAuIsTVjh4snhbMXWhBpeO5YqOQXaM5fvgu6ITEBHVSXJwwn+4nbxwA33d8WCpEups3lhp7U7+cUV0BLJj9l2+cxOAi7+LTkFEVKeswKHIqlSKjmHXpgb5YmCaEZWlvLHSFuSllSLzYqHoGGSn7Lt8H3oPAKe8E5Fl43byYj0WHIDWieUw6LhKhi3h6DeJYr/luyQLOLNZdAoiojoZnf3wfjq3kxfBRSHHSr8AOCSUcpzGBqWczkdxHjc4I/Oz3/Idsx4waEWnICKq03m/kdAY7fdLtSihzio87eSNiiTeWGmrJAk4tStddAyyQ/b5FV1TChzdKDoFEVG9Pi7pIzqC3bndyw1zK5ygzuCoqK1LOJwFTTnn8ZN52Wf5PrYR0BSLTkFEVCetVzt8n8Pt5M3pvkAfjMgCKor4k1F7oNcYcG5/pugYZGfsr3wbdMCR9aJTEBHVK4bbyZvVoyEBaH+hAnoN96y0J2f2pMNo4M20ZD72V77Pfg+UZIhOQURUJwkyvJ3bU3QMu6CSy/BCQCCc4kvBPZ/tj7pQg6Tj3HSHzMf+yveh90QnICKqV2lgHxwvdhcdw+YFOTniny6+qLxQIjoKCXSKyw6SGdlX+U7eB+ScFZ2CiKhevyuGio5g83p6uuJhrSvU6WWio5BguamlyLrEe8HIPOyrfJ/4UnQCIqJ6SQ5OWJ3ZSXQMm3Z3gBfuypGhvEAjOgpZiPgDnJJK5mE/5buyBEj4RXQKIqJ6cTv5lvVwiD86J2mhq+SNlfS3pON50FboRccgO2A/5fvcFkDHNVuJyPJt1g0UHcEmOcpk+GdgIDzi1ZCMvLOSqtNrDLh4NEd0DLID9lO+T34lOgERUb2Mzn5Ylx4mOobN8VU64HkPP+gSeWMl3Vz8Aa753RRhYWF45513RMewePZRvvOTgCsxolMQEdWL28k3v84eznjU6A51qlp0FLJwuamluJrBz5P6bNy4EV5eXqJjmFRWVmL27NmIjo6Gg4MDJkyYUOtxe/bswW233QaVSoX27dtj48aNNY55//33ERYWBicnJ/Tt2xexsbE1rrV48WL4+vrCzc0NEydORE5O435iYh9f4TnqTURW4qOSvqIj2JRRfp64L98BZfmVoqOQlbCn0W+t1jZ2cjUYDHB2dsbSpUsxYsSIWo9JTk7GuHHjMGzYMJw8eRLLly/H/Pnz8dtvv5mO+eabb/D444/jxRdfxPHjx9G9e3eMHj0aubl/rwP/2GOP4ZdffsHmzZuxd+9eZGZm4r777mtUXtsv30YDcGqT6BRERPXSerXHDzkBomPYjLnB/uiZrIOmnDfRUcMlxmbDoBO74+XQoUPx6KOPYvny5fD29kZgYCA+/PBDlJWVYc6cOXB3d0f79u2xffv2aq/bu3cv+vTpA5VKheDgYDzzzDPQ6/XVzrtkyRIsX74cfn5+GD16NADg7NmzuOuuu+Dm5obAwEDMmDED+fn5tWbbs2cP5syZg+LiYshkMshkMqxatcr0fHl5OebOnQt3d3e0adMGH3zwQbXXP/3004iKioKLiwsiIiLwwgsvQKfTmZ5ftWoVevTogS+++AJhYWHw9PTE1KlTUVpaetPfL1dXV6xfvx4LFixAUFBQrcds2LAB4eHhWL16NTp16oQlS5Zg0qRJePvtt03HvPXWW1iwYAHmzJmDzp07Y8OGDXBxccEnn3wCACguLsbHH3+Mt956C8OHD0evXr3w6aef4tChQzhy5MhN893I9sv3pd1Aqf18F0tE1usIt5NvFnIATwcFwjdBDaOBN1ZS42jK9Lh8Mk90DHz22Wfw8/NDbGwsHn30USxcuBD3338/+vfvj+PHj2PUqFGYMWMGysurFpPIyMjA2LFjcfvtt+PUqVNYv349Pv74Y7z88ss1zqtUKnHw4EFs2LABRUVFGD58OHr27ImjR49ix44dyMnJweTJk2vN1b9/f7zzzjvw8PBAVlYWsrKysGLFCtPzq1evRu/evXHixAksWrQICxcuRGJioul5d3d3bNy4EfHx8VizZg0+/PDDagUYAC5duoQff/wRW7duxdatW7F37168/vrrTfr9PHz4cI1R8dGjR+Pw4cMAqn4KcOzYsWrHyOVyjBgxwnTMsWPHoNPpqh3TsWNHtGnTxnRMQ9h++eaUEyKyAhJkeIfbyTeZp4MCK70DYDzPGyvp1sUfFD9o1717dzz//POIjIzEs88+CycnJ/j5+WHBggWIjIzEypUrcfXqVZw+fRoAsG7dOrRu3Rpr165Fx44dMWHCBLz00ktYvXo1jMa/R/IjIyPx5ptvokOHDujQoQPWrl2Lnj174tVXX0XHjh3Rs2dPfPLJJ9i9ezcuXLhQI5dSqYSnpydkMhmCgoIQFBQENzc30/Njx47FokWL0L59ezz99NPw8/PD7t27Tc8///zz6N+/P8LCwjB+/HisWLEC3377bbVrGI1GbNy4EV27dsWgQYMwY8YM7Nq1q0m/n9nZ2QgMDKz2WGBgIEpKSlBRUYH8/HwYDIZaj8nOzjadQ6lU1pjvfv0xDeFwa2/BSlQUAud/FZ2CiKheJYF9cTzVrf4D6aYi3ZzxoM4Z6uSb/3iaqCHSEwtRkl8BDz9nYRm6detm+n+FQgFfX19ER0ebHrtWEq/NR05ISEC/fv0gk8lMxwwYMABqtRrp6elo06YNAKBXr17VrnPq1Cns3r27WoG+5tKlS4iKirrl3NcK+vVzpr/55hu8++67uHTpEtRqNfR6PTw8PKqdIywsDO7u7qaPg4ODq53D2tl2+T7zHWDg7mVEZPl+cxgiOoJVG+rrgUE5gFpdIToK2QIJSDiUhb73RAiL4OjoWO1jmUxW7bFrJfv6Ue2GcHV1rfaxWq3G+PHj8cYbb9Q4Njg4uFHnBmrPfS3j4cOHMX36dLz00ksYPXo0PD09sWnTJqxevbrB57hVQUFBNVYlycnJgYeHB5ydnaFQKKBQKGo95to88qCgIGi1WhQVFVUb/b7+mIaw7WknJ78WnYCIqF6SgzPeSud28rfqwWA/3JGqR6VaV//BRA10/nCWVW3G1KlTJxw+fBiS9HfmgwcPwt3dHaGhoTd93W233YZz584hLCwM7du3r/brxqJ+jVKphMHQ+B1iDx06hLZt2+K5555D7969ERkZidTU1Eaf51b069evxtSVnTt3ol+/fgCq3lOvXr2qHWM0GrFr1y7TMb169YKjo2O1YxITE5GWlmY6piFst3znJgCZx0WnICKqV2bgUGRruJ18Y8kArAgORHBCGQx66ylJZB3UhRpkXCwSHaPBFi1ahCtXruDRRx/F+fPn8dNPP+HFF1/E448/Drn85nVv8eLFKCgowLRp0xAXF4dLly7ht99+w5w5c25asMPCwqBWq7Fr1y7k5+ebbvqsT2RkJNLS0rBp0yZcunQJ7777LrZs2XJL7/dG8fHxOHnyJAoKClBcXIyTJ0/i5MmTpucfeeQRXL58GU899RTOnz+PdevW4dtvv8Vjjz1mOubxxx/Hhx9+iM8++wwJCQlYuHChaYUZAPD09MS8efPw+OOPY/fu3Th27BjmzJmDfv364Y477mhwVtuddnLiS9EJiIgaZLNugOgIVsfVQY4Vnn4oT+CNldRyLsblILSDt+gYDdKqVSts27YNTz75JLp37w4fHx/MmzcPzz//fJ2vCwkJwcGDB/H0009j1KhR0Gg0aNu2LcaMGXPT0t6/f3888sgjmDJlCq5evYoXX3yx2nKDN3PPPffgsccew5IlS6DRaDBu3Di88MILDXptfcaOHVttFL1nz6ob2K/9JCA8PBy//vorHnvsMaxZswahoaH46KOPTMstAsCUKVOQl5eHlStXIjs7Gz169MCOHTuq3YT59ttvQy6XY+LEidBoNBg9ejTWrVvXqKwy6fqfT9gKgx54qxNQZjuT84nINhld/NCp6B3uatkIYS4qzJHcoM5q2Ggb0a1ycnXE7DcHQKHg309qPrb52ZS0k8WbiKxCgu8oFu9G6O/jjplqJxZvMovKMh2uxBeIjkE2xja/4nNtbyKyEh+W9BEdwWpMCfLFkHQjKkpsY0tssg5JRzmYR83L9uZ8V5YAF34TnYKIqF5a70j8mMXt5BtiWUgAlAml4H2VZG6XT+VBrzPAwVEhOgrZCNsb+b74O2DgqAgRWb7DbiPqP8jOOSvkWOkfAGV8KcDiTQLoKg1IO8upJ9R8bK98J/wiOgERUb0kyLCG28nXKdRZiWecfFBxkTtWkliXTnDqCTUf25p2otcASbvqP46ISLCSwD7cTr4Ovb3cMLZIAXVRmegoREg5nQ+D3giFg+2NWZL52dZn0eU9gJYjJERk+XYohomOYLEmBPpgVBZQUcQphGQZtJUGrnpCzca2yvf5raITEBHVS3JwxuoMbidfm0UhAYi6UAGdpvFbVxO1pEvHOfWEmoftlG+jEUjcLjoFEVG9MoKGIVfjKDqGRVHJZXg+IBCu8aWwwa3fyAYkn86HwWAUHYNsgO2U7ysxQFme6BRERPXarO0vOoJFCXRyxD/dfKG5wK3iyXJpyvXIvFAkOgbZANsp35xyQkRWwOjijw3pYaJjWIxuni54ROcGdRpvrCTLl3ruqugIZANsp3wnbhOdgIioXvHcTt5kbIAXxucqUH61UnQUogZJO8vyTU1nG/8C5CcBBZdFpyAiqteHJbeLjmARFoT4IzpJC22FXnQUogYrzC5HydUK0THIytlG+b74u+gERET10nhH4acc+95O3kEmw7OBgfCKV8No5J2VZH3SznHJQWoalm8iIjOx9+3kfRwd8IKnH/SJvLGSrFcqp55QE1n/DpfaMiD1kOgURER1kiDDOzk9RMcQppO7M6ZUOkGdohYdhahJMhILudslNYn1f+Zc3gsYNKJTEBHVqSSwL06W2Od28iP9PDGxwAFlebyxkqyfTmNAVlKR6Bhkxay/fHPKCRFZge12up387GA/3Jaig6aMN1aS7UjlvG9qAhso3ztFJyAiqpPk4Iy3MjqKjmFWcgBPBQfCP6EMRj1vrCTbksb1vqkJrLt855wDStJFpyAiqlNG0HC72k7ew1GBld4BkBJ4YyXZpoLMMpQWcBoV3RrrLt9Jf4hOQERUr2/saDv59q5OeELhibLkUtFRiFoUR7/pVll3+U49LDoBEVGd7Gk7+UG+HphWooQ6m5uQkO3jet90q6x7qcH0WNEJiIjqdM53FHQFMtExWty0IF+0vVSJSp1RdBQis8i4UAhJkiCT2f7fb2pe1jvynX8RKOePfIjIsn1Q3Ed0hBYlA/B4cABCz5fDwOJNdkRTrkdBZpnoGGSFrLd8px0RnYCIqE4a7w74JddfdIwW4+ogx0q/ACgSOL+b7BPX+6ZbYb3l+0qM6ARERHU6ZMPbybdxUeEppTfKk1i8yX5lJhWLjkBWyHrnfLN8E5EFk2RyvJ3TXXSMFnGHtztGFcihLi4XHYVIqKxLRaIjkBWyzpHv8oKqOd9ERBaqOLAvTtvgdvKTAn0wLMOIimKt6ChEwqkLNFzvmxrNOsv3lVgA3DGNiCzXdvlQ0RGa3dKQAIRfqIBeyxsria7h6Dc1lpWWb045ISLLJTm64K1029lO3kkuxwv+gVDFl3Lcg+gGWZz3TY3E8k1E1MzSA4chT2sb28kHOynxrIsPKi9yq3ii2rB8U2NZ3w2XBh2QcVx0CiKim/pGM0B0hGZxm6crxpc4Qp3NtYyJbqYgUw1NhR4qZ+urVCSG9Y18Z50G9Ny6mIgsk8E1AP/NaCs6RpPdE+CNMTkylBdqREchsmiSBGRf4ug3NZz1fZt2hZvrEJHlivcZCd1V695uemFIANzPq6EzcoL39ZIyT+OPU98gLf8iSsqvYsGol9A9fKDp+ZOX9+NAwi9Iy7uAck0pnpn4X4T6ta/znAaDHr+f/BoxF35HUVk+Aj1b496+C9C5zd87o8Zd/AM/xXwEja4Cd3QYjYn9F5meu1qajbW/PoWn7lsPZ6Vr879papCspCK07eorOgZZCesb+eZ8byKyYP8t6is6wi1zlMnwXGAg3OJLIbF416DRV6CVbztMGbi01ue1+kq0C+qKCX0XNPicv8R9ggPxW3H/gEfx/ORPMLDzeHz4+4u48tdyuuqKYny9dzX+ccfDWDLuDcRd3IUzqYdNr/9m/xrc22cBi7dgWRz5pkawvpHvNJZvIrJMGp8O2JrpJzrGLfFXOWKJygvqRN5YeTNd2vRFlzY3/+aqT9RIAFWj0Q0Ve/EPjO75gOm8g7rcg/MZx/Hnqc2Ydec/kV+aBSelK3q1HwYAiArpgZzCNES37YejSX9CIXdAj4hBTXhX1BzyrpRCkiTIZNb9Uy8yD+sa+S5MAdQN/6JGRGROB12tczv5aA8XLNa7QZ2mFh3F7ugNWjg6KKs9plQocSn7LAAgwLMVdHoNruRfRFllCVLzEhHiG4FyTSm2xn2KyQMfFRGbbqCrNKAkn/ejUcNY18j3lVjRCYiIaiXJ5Hg7u4foGI02xt8LvTIMKCvnLn0idGp9O/48/R3aB3eDn0cIEjOO42TKAUjGqo2MXFTumDHsaXy++w3o9Br0iRqJzq1vx1d7/o0hXSbgakk2/rvjBRiMeoztPRM9I4YIfkf2K/+KGp7+LqJjkBWwrvKdxpsticgyFQfegTMp1jXvdn6IP3wTy6A1cH63KJP6L8b/9q3Gv76ZAxkAP48Q3BE1GkcSd5iO6R4+sNqNnRczTyGjIBn3D3gUqzbNxJw7n4OHiw/+vWUx2gd3g7uzt4B3QvnparS7LUB0DLIC1lW+M0+ITkBEVKttcusZcZQDeCooEIb4EnCjeLHcnb3w0Oh/QafXokxTDE8XP/wU8yF8PYJrPV5n0OKbA2swa9gzyCvJgNFoQGRIdwBAgGcoUnISEB3W35xvgf6Sn85pW9Qw1jXnO/+C6ARERDVIji5420q2k/d2dMCLXv4wnOeNlZbE0UEJL1d/GI0GnEzej25tay/Qvx3/Cp1b347W/lEwSkYYJYPpOYNRD6PEb6dEyb9SKjoCWQnrGfkuzgC0/K6SiCxPeuBw5CVZ/nbyHdycMU3rDHUKv5beCo2uAnnFGaaPr5ZmIz0/CS4qd/i4B6KssgSF6lwUl18FAOQUXQEAeLj4wMPFBwDw+Z+vw9PVD/f2nQ8ASMlJQFFZPkL92qGoLB/bjn4OSZIwosfUGtfPKkzBsUu78czE/wIAAr3aQCaT4dD5bfBw9kFOURraBnRo0d8Dujl1oQaVZTo4uVr+1wISy3rKd36i6ARERLXapLH8H/MP8/PEgCwJZWVckeFWpeYl4t1fnjB9/MPh9QCAvlGjMGPY0ziTeghf7vm36flPd70MALir10yM6z0LAFCgzq22HJ3OoMXWuE+QX5oFlaMzurTui5nDn4GLyq3atSVJwv/2vY37+i2EytEZAKB0UOHBoU/h2wPvQm/QYfKAR+Hl6t8yb54aJD9djdAOnHNPdZNJkmQdd9rE/BfY/pToFERE1RhcA9Cx8G3ojJa7vu+MYD+0ulgOg946vtwTWasBk9qjx4g2omOQhbOeke88jnwTkeU55zPKYreTlwFYERwIJJTAUO/RRNRUV3nTJTWA9ZRv3mxJRBZoQ1Ef0RFq5e6gwBMevihL4I2VROaSx/JNDcDyTUR0iyp9OmKbBW4nH+7ihNlGF6gvc/UFInMqzCqDQW+EwsG6FpMj87KOz47KYkCdIzoFEVE1hyxwO/mBvu54sFQJdTZvrCQyN6NBQmF2megYZOGso3zncdSbiCxL1Xby3UXHqGZqkC8GphlRWaoTHYXIbhXl8Btfqpt1TDvhMoNEZGGKAvtZ1HbyjwUHwOF8KbhTPJFYxXnloiOQhbOS8s2RbyKyLNtlg0VHAAC4KORY4e2HigTO7yayBCV5HPmmullH+ea0EyKyIJKjK1ZniN9OPtRZhQVyN6iTWLyJLEUxyzfVwzrKN0e+iciCXAkcjquCt5O/3csNdxUpoC7ij7iJLAnLN9XH8su3XgsUpohOQURk8j9NP6HXvy/QBx1StKjQaIXmIKKayoo0MOiMUDhax5oWZH6W/5lRcAmQuDcbEVkGg2sgPspoK+z6S0IC0P5CBfQafl0kskSSBJRc5eg33Zzll29uK09EFuSczyjojObfTl4ll+GFgEA4x5dC4oomRBatOJflm27O8qed5F8UnYCIyGR90e1mv2aQkyMWOnhCfYFbxRNZA877prpYQfnmyDcRWYZKn07Ybubt5Ht6uuKeUkeouWsekdUozmf5ppuz/PJdlCY6ARERAOCgmbeTvzvAC93S9Civ1Jj1ukTUNJx2QnWx/PKtzhGdgIgIkkyOt7K7me16D4f4w/N8GXRGTvAmsjYlHPmmOlj+DZfqPNEJiIhQFNgP50pbfjt5R5kM/wwMhEe8GhKLN5FVKrlawb+/dFOWXb61ZYCO8xyJSLxt8iEtfg1fpQOe9/CDLpE3VhJZM6NeQoVaJzoGWSjLLt9lHPUmIvEkR1e8ld6hRa/R2cMZjxrdoU5Vt+h1iMg8Kkq5CRbVzrLLN6ecEJEFSAu8E1e1Lbed/Cg/T9yX74Cy/MoWuwYRmVd5Mcs31c6yb7gsyxWdgIgIm1pwO/k5wX4IuFAOjYHzQ4lsSTlHvukmLLt8q1m+iUgsg2sQPkhv3eznlQN4MjgQxoQSGJv97EQkWnkJyzfVzrKnnXDONxEJdtZ3JAxS836p9HRQYKV3AIwJvLGSyFaxfNPNcOSbiKgO6wv7NOv5It2c8aDOGerk0mY9LxFZlgqWb7oJyy7fnPNNRAJV+nbGjgzfZjvfUF8PDMoB1GpuwEFk6zjnm27Gsss3VzshIoEOuNzZbOd6MNgPoRfLUannjZVE9oDTTuhmLLt8c+SbiASRZAqszure5PPIADwRHAhZQgkMTY9FRFaC5ZtuxrJvuOTINxEJUhjYDwlqlyadw9VBjpW+AZDxxkoiu1Op1nGLeaqV5ZZvvQbQFItOQUR2qqnbyYe5qPCUozfKL/HGSiJ7JBm5xTzVznKnnXClEyISRFK64u30qFt+fX8fd4zIl0NdUt6MqYjI2lSUauHioRQdgyyM5Y58c743EQmSFnDr28lPDvLFkHQjlxkjImjK9aIjkAWy4JFvzvcmIjG+rux/S69bFhIAZUIpuKAJEQGATsvbrKkmyy3fHPkmIgEMbsH4KCO0Ua9xVsjxpI8fKuI5v5uI/qbXsHxTTZZbviu5OgARmd8Z75Ew5Dd8Rl6osxIL5B5QX2TxJqLqOPJNtbHc8m3gfEkiMr91jdhOvreXG8YWKaAuKmvBRERkrTjyTbWx4PLN5XmIyLwqfLvg9wyfBh07IdAHnVO0qNBwoICIaqfTGEVHIAtkueXbyPJNROZ1wLlh28kvCgmAW0IpdLyxkojqoNNwtROqyXLLN6edEJEZSTIFVmd3q/MYlVyGJ/0CoIkvAXs3EdVHp+XIN9Vkuet8c9oJEZlRYVB/nK9jO/lAJ0f8080Xmgu8GZyIGoZzvqk2FjzyzfJNRObzKwbf9Lluni74h1oFdTZvrCSihuNqJ1QbCy7fnHZCROYhKV3xVnqHWp8bG+CFnlcMKK+oNHMqIrJ2HPmm2ljutBMjb1IgIvNIDRiBQl3NsYgFIf6ITtJCW8GvR0TUeBz5ptpw5JuI7N7Xlf2qfewgk+HJwADo40vA26WI6FbpOPJNtbDg8s0530TU8gxuwfj4uu3kfRwdsMzVG+rzvLGSiJrGaOC6SFQTyzcR2bXTPqNM28l3cnfGlEonqFPUglMREZGtsuDyzWknRNTy1hfcDgAY6eeJvllGlJXxxkoiah4ymegEZIkst3xzh0siamHXtpOfHeyHwIvl0Oj5I2Iiak5s31ST5ZZvTjshoha23/lOPBUcCCmBN1YSEZF5sHwTkV1K9YtAueYO+OSXQ+/jCJ1Ogk5rrNoOmgPgRNQMOO2EamPB5Ztzvomo+aT7tEFcUBRinZSIrchGbmU+VNJzmO3ZGUOOVcLhRAIAQJLJILl6QnL3htHVC0YXDxid3WFwdodR5QqDowsMjs4wODhBr1DBIFdCD0fo4QC9UQGdQQa9XgadnmWeyO6xfFMtLLd8c5MdImqCbK9WiAvugBhnJ8RV5iKzIheouABU/H2MRmbAf/3P4L9jgNuHtcGMC4EI3p8IqbgIUBdB0Qw5rpV5o7sXJBcvGF09YHT2gMHJrarMK13/LvPyv8q87FqZl0NvlEOnl3FknojIRlhu+ebINxE1Qr5bAOJCOyPGxRVxmnyklWcBlUlAAxcviVNlIi46E25dlZif0wt3xJZAfu5ik3PJJAkydRHk6qImnwu4VuY9YHT3huTiBYOrByQn96rR+RvKvEGhgl528zKv10nQaQyQWOaJiMzGcsu3TC46ARFZsEJXX8SFdkGsqzvidIW4rE4HNJcBTdPOq5Zp8U7QKeAeYMjIdph63ht++xMglZU1T/AmqirzxZCriwEAjk083/Vl3ujiCaOrByQnj+vK/LVpNs51lnm9XoJOyzJPdD0ZJ31TLSy3fDu6iE5ARBakxNkTR0O7Ic7dE7G6IlxUX4GkTQFa8Idke51TsbdnKny6OWNBdm/0OpwPXExpuQsKcGOZbw5GV08Y3bxgdPOs+v+/RuZvnGZjUKiglyuhhxJ6KGCQFNAZWOaJyLZZbvlWuopOQEQClanccSw0GnEePogxlCBRnQajPhUoNH+WAkUF3mh1EpgE3FXWAfedc4XXwXhIldyQpzbysmLIy4qBnOY5n9HVA0Y3bxhdPavmz19/A6zSBUZHF+irlXlHGOAAvanMA3o9qsq81gCJ60qSmXDgm2rD8k1EFqFC6YITraIR6+WHOGMZ4kvToDemA0XpoqNVs931Erb3AYJ6ueGh9GhEH8yClGpZGW2NvKwE8rKSZjtfVZn3guTqVTUy7+z+9w2wKte/y7xcBYPihtVsjHLoDddugGWZJ6LGY/kmIiE0Dk44FRqNWK8AxKECZ0pToTNmAUVZoqM1SLZCjf9rewJoC9xX2hnjTyvheuhs1RArWbS/y3xas5zP6OwGyd2naqqNiweMLh7VyrzBwRkGRycYFE7Vl6b8a2TeVOav3QDLMm8zZHIOfVNNLN9EZBY6uSPOhEYj1jsIcTItTqlToTHkAMXNNDdBoB/cL+CHAUBYXy8sSI1A1IE0SJnZomORmcgr1ECFGorcZizzbt5/zZn3ql7mlS5/rTX/18i8XFl1EyzLvEVyVDXHgqVkayy4fLuJTkBETWCQKXCuVVfE+oYgVq7HydJUVBjygZJ80dFaTIpDEZ5rdxyKdjJMLYrG6BOAU1w8YDCIjkZWxFTm8640y/mMTq5Vm0a5eVWVeWd3GJ3cYHBy+7vM/zVn/sYyrzfIobuuzOs1RhiNvAO2oRyULN9Uk+WWb652QmRVjDI5EoI7Ic6vDWIVRhxXp6JMXwiUCLhDUjADJHzllYCvhgEdB/pjbnIbhO+7DCnPdr/xIMslrywDKsugyGueexNMZd71r02jXDxgdHI3lXnjX2XetAOszLFqqo2pzF93A6yNl3mOfFNtLLd8c9oJkUWTIMOFoI6I82+LWEfgWNkVlGhLgdJzoqNZlPOO+XgqKh/KSAVmFvTAsGNaOB5PANfPI2vV7GVe5XLdyLynqcyb5sxfNzKvv77MG+XQS3Lo9TLoTKvZGGE0WM7fLQcl9yyhmiy4fHPaCZGluRwQidiAcMQq5ThaloFCbTGgjhcdyypoZQZ85HsWH40Ceg4NxayLQWi1/yKkwiLR0YiEkmvKAU05FPkZzXI+SeUMo+kGWE8YXa6tM//XNBvltTLvZCrzBjhAJymqCv21Mq/7a2S+CWXekdNOqBYWXL457YRItDS/cMQGtkesyhFx5ZnI1xQAZecBy9js0WqdUGbhRJcsuHRyxNz82zAgVg3FmQuiYxHZBJmmAgpNRvOWeTdvSH+NzBtcPKpvGnVDmTf8tQOsTlLA3ZmrH1FNMkmy0J99ntsCbJ4tOgWRXcn0boOY4CjEOakQW5mFnArOUTaXAZWt8cB5XwTsT4Ck5nc3RLYg8Lnn4DPjQdExyMJY8Mg3p50QtbQczxDEhnREnLMzYjW5yCjPASouABWik9mfg05XcLDHFXh2c8JDOb3R+3ABZImXRccioiaQu7PLUE0WXL55wyVRc7vq5o+4Vl0Q6+qKWG0+UsuygMokgLukW4xieSX+HXwSuA8YWRaJ+xM84H3gHKQK/iERWRuFu7voCGSBLLd8c6lBoiYrcvFBXGgXxLp5Ik5XgEvqdEB7GdCKTkYNsdM1GTt7AwE93bAgMxo9DuZASm6ejVyIqOXJ3Vi+qSbLLd+cdkLUaKVOnjjaOhqxbl6IMxTjQmkaJF0qYH9LbduUXIUar7Q+AUwF7i3tiHvPOMPt0FlApxMdjYjqIHfjT/GpJsu94bIkC3iro+gURBatXOWGY6HRiPPwQaxRjfOlaTBI3E3RHrQxeGF+WgQ6HUiHlJ4pOg4R1aLd779B2aaN6BhkYSy3fFcWA6/zE5boepWOzjgRGo04T3/ESGWIL02DXuJSVvZMJgGTizti7GkFnGPOVW0dSEQWIeroUSg4+k03sNzybTQA/+cjOgWRUFqFCqdCoxHrHYhYVOBMaSp0Rk41oNpF6n0xL7kt2u1PhpSTJzoOkV2TOTuj44njomOQBbLc8g0Ar7UBNMWiUxCZjU7uiLOtuiLWJxhxMi1OqlOhMWhExyIr4yDJ8WBhR4w4boTy6DluZU8kgGNoKNr/sVN0DLJAll2+1/UDcrl1Ndkug0yB+FZdEOvTCnEKA46rU1Gh5yLb1HyitQGYk9QKrfcnQSrgnbdE5uLcvTvCvtkkOgZZIMtd7QQAPFqxfJNNMcrkSAzqiFj/tohVGHG8LA1qXRFQWiQ6GtmoM8pcPN45F06dHDAnvycGx1VAceq86FhENk/h7yc6Alkoyy7fnq1EJyBqEgkyJAVGIdY/DLFKGY6WXUGJthQoPSc6GtmZSpke6/3PYP1YoO+dbfFgYgCC9p2HVFoqOhqRTXLwZfmm2ll2+fYIFZ2AqNEuB7RHXEAEYpUKHC3PQIGmCChLAMpEJyOqEqPKQEy3DLh3VWF+Ti/0jSmGPCFJdCwim+Lgx/JNtbPs8s2Rb7ICV3zDEBvUHrEqJeIqMpFXWQCUnWfZJotXKtfg7eBTwARg+Kj2mJzgCd/98ZAqeN8BUVM5+PmKjkAWyrLLtwfLN1meLO/WiA2KQqyzCrGVOciuyAPKLwDlopMR3bo/XVLwZy/Ar6crFmR2Rc9DucClVNGxiKyWgiPfdBOWXb49Oe2ExMv1DEZscCfEuTgjVpOH9PJsoPIiUCk6GVHzy5eX4bXQE8BkYKy6A+475wrPg+cgabjkJVFjcNoJ3YxlLzWoqwReCRSdguzMVTd/xLXqjDhXN8RqC5BSliE6EpFQrQweWHClHbocyIR0hX8fiBqi3c7foWzdWnQMskCWXb4B4M12QHm+6BRkw4pdvHG0VVfEunsiVleAS+oMSLDsvxZEIsgkYFJJB4w77QCXI9zKvjZr8/Ow7urVao+FK5X4NTyi3tduKynBiqxMDHdzw9pWf//k95OCq/ikoAAAMM/HB3N8/p5LfKqiAv/KycamtmFwkMma6V1Qc+hw8gTkTk6iY5AFsuxpJ0DVTZcs39SM1E4eOBoajVh3b8QZinFBfQVGfSrA/UeI6iTJgM2eidg8CIjo5435KeGI3J8CKTtXdDSL0l6pxMet25g+bsg/tBk6Lf6dl4tezs7VHk+srMTa/HysaxUKCcCijHQMcHVFlMoJeknCSznZeCkwiMXbwij8/Fi86aYsv3x7hAJZp0SnICtWrnTF8dBoxHr6Is6oRkJpGgyGK0DRFdHRiKzWZYdC/LN9IRTtZZhe2A0jT0hQxZ0DjEbR0YRTyGTwd2j4P68GScJTmVlY4uuHYxXlKLnu9/CyVosolQp3uLoCAKJUqr8ec8InBQXo7eyC6BsKO4nH6SZUF8sv31xukBqp0tEZJ0OjEevpjzipHGfVqdAbM4GiTNHRiGyOARI+947H58OBLgODMCc5FGH7kmDMLxAdTZg0rRZDkpKgksvQ3ckZj/n7I8TR8abHr7uaDx8HBSZ6eeFYRfVlk6JUKqRotcjU6SABSNVqEalUIU2rxZbiInwXFtayb4ZuiWNrLhhBN2f55ZvLDVI9dAolToVGI847CDGoxJnSVGiN2UBxtuhoRHblnDIXKzrkQhWlwKyrPTD0qAYOJ88DFn5rUXPq5uSMV4KDEe6oRJ5Bj3X5VzEjLRU/h4fDVa6ocfyx8nL8UFyMH9qG1Xq+dioVlvv7Y/6Vqp/ULff3RzuVCnOvpOEJ/wAcKCvD+/n5cJDJ8M+AQPR2cWnJt0cNpLxu2hHRjSy/fHO5QbqBXu6As626ItYnBLFyLU6VpqLSkAcU54mORkQANDIDPvA7iw/GAL2HtcaMi4EI2ZcIqbhEdLQWN9jNzfT/HVBVxkdcvoQdJaWY6OVV7dgyowHPZGfhpcAgeNcxTWWqlzemenmbPv6xuBiucjl6ODtjXPJlfNM2DDl6HZ7IzMTOiAgo5fLmflvUSBz5prqwfJPFM8gUSAjpjFjfUMQqDDihTkO5vgAosd8faxNZi6OqTBztmgm3LkrMy+mFfrElkJ+7KDqW2XgoFAhTKpGq09Z4Lk2rQ4ZOh8UZ6abHrs32jk48j1/DI9BGqaz2mkK9Huuu5uPz1m1wurICYUql6ZceElJ0VfPBSSxlG458081ZfvnmtBO7I0GGxOBOiPVrgzhHCcfUV1CqKwZKi0VHI6JbpJZpsSboFNbcAwwaFYFpCT7w358AqaxMdLQWVWY0Ik2rxXgPjxrPRSiV+CksvNpja/LzUGY04p8BgQiqZZ7463m5mOntjSBHR5ytrITuuik9BkmCwX5m+Fg03nBJdbH88u0eDMjkgMQ76G1ZUmAHxAaEIdZBhqPl6SjWlgDqeNGxiKgF7HdKw/6eafDu7owF2b3R+9BV4GKy6FjN4s3cXAxzc0OIowNy9Xqszc+HQibDOPeq8v1MViYCHBzwuH8AVHI5IlWqaq/3+GvKyI2PA8ChsjKkaLV4LSgYANDVyQnJWi32qdXI1ushl8kQfsNIOZmfzNkZDv7+omOQBbP88q1wANyCgFKuVGFLUvzbITYgArEqB8SVZ6JAUwioE0THIiIzKpRX4M2Qk8AkYHRZFCaec4P3wXhIlZWio92yHL0OKzIzUWQ0wEehwG3Ozvhfm7bw+WtOd5ZOh1uZkV1pNOLlnBysDgmB/K81vYMcHfFcQCCey86CUibDa0HBcOJ8b+GUoZwuS3Wz/B0uAWDj3UDKftEpqAmu+LZFXGAkYp2UiKvIQm7l1fpfRER2J8DoiofTO6DbwUxIKen1v4DIwrgNH47W694XHYMsmOWPfANAYBeWbyuT7RWK2OAOiHV2QlxlDjIrcoGKC0CF6GREZMly5WX4V5vjQBvgHyWdcM8ZFVwPnwN0OtHRiBqE872pPlZSvruKTkD1yHcPRGyrzoh1cUGsJg9XyrOByouA9f70mIgE2+JxEVsGAG37emF+WgQ67k+DlMn1+8myKcPDREcgC2cd5TuI5dvSFLr6Ija0C+Jc3RGrLUByWQaguQRoRCcjIluT6lCEFyKOQxEhw5SirhhzUgan2HjAYBAdjagGVVSU6Ahk4axjzreuEng1BJD4hVaUYmcvHA2NRpybJ2L1RUhSX4EEy//UISLb1EHnh3nJbRC+7zKkvHzRcYhMoo7GQXHdZktEN7KO8g0A7/cF8s6LTmE31E4eON6qK2I9fBBrKEGiOg1GLvdIRBZGKSkws6AThh3TwvF4gl1tZU+WxzEkBO3/3CU6Blk465h2AlTN+2b5bjEVShecCO2GGA9fxElliC9NhcGYDhRxtQEislxamQEf+Z7FR6OAHkNbYVZSMEL3XYRUWCQ6GtkhTjmhhrCe8h3UFTj7negUNkPj4ISTraMR6xmAOFTgTGkK9MZMoJjrqRORdTqpzMbJztlw6eiIufm3YUCsGoozF0THIjui6tBBdASyAtZTvgOjRSewajqFEqdbRSPWJwhxqMSp0lRojTlAcY7oaEREzapcrsPagNNYezfQf0Q4Hkj0ReD+85BK1aKjkY1TRUWKjkBWwHrmfJdmA6v5HWVD6eUOONeqC+J8WiFGrsWp0jRUGLjuHxHZJ0/JCfOzO6LPkULIzl8SHYdsVMQvP0MVyQJOdbOe8g0Ab7YDynlXe22MMjkSgjsj1i8UsQojTqjTUKYvFx2LiMjijCgPw/3xnvA5cA5SBQclqHnIHB3R4cRxyBysZ1IBiWFd5fvze4HLe0SnsAgSZLgQ1Amx/m0Q6wAcK0tDqY4/UiUiaqgAgxvmZ0aix6Ec4HKa6Dhk5VQdOyLixy2iY5AVsK5vzwK72nX5vhQQhdiAcMQp5Thalo5CbTGgjhcdi4jIKuUq1Hi19QlgCnCPuhPuPeMEj0PnIGm1oqORFXLqwJVOqGGsq3wH2ddNl6l+EYgNbIdYlQPiyjNxVVMIlCUAZaKTERHZlp/dLuLnfkDo7Z5YcKUdOh9Mh3SFqz9Rw3GZQWoo6yrfgba9zXyGTxvEBkUh1kmJ2Ips5FbmA+WJAKduExGZRbpDMV4MPw5ZGDC5pCvGnpLDOSYe0OtFRyML59TFtjsKNR/rmvNt0FVtM2+wjR8JZnu1QlxwB8Q6OyNOk4uMci77R0RkaSJ1vpiX0hbt9idDyskTHYcskVyOqNhYKNxcRSchK2Bd5RsA1g8Ecs6ITnFL8t0CEBfaGbEurojTXkVqGX+kSURkLRwkOR4s6oQRx/RQHksAjEbRkchCqCIjEfHLz6JjkJWwrmknABDYxWrKd5GLD+JCuyLGzR1xukJcVqcDmsuARnQyIiJqLL3MiI3e57BxBBA9OAhzLoWi9b6LkAoKRUcjwZy6dxMdgayI9ZXvoK7AadEhalfi7ImjodGIc/dCrK4IF9VXIOlSAH5dJiKyKWeUuXi8Uy5UHRWYk98Tg49WwOHkedGxSBDnbizf1HDWN+0k9TDw6RjRKQAAZSp3HAuNRpyHD2INJTivToNR4o8hiYjsUR9NK8y4EICgfYmQSkpExyEzCv/pRzh14C7c1DBy0QEarVUvwMFJyKUrlC44FN4Xa3qOw/TuQzGwlT8WG9Oxseg04ktTWLyJiOxYrCoDj0afwNyFBhya0wvGzu1FRyIzkLu6CtlSftWqVZDJZNV+dezY0fR8ZWUlFi9eDF9fX7i5uWHixInIyam+sMPSpUvRq1cvqFQq9OjRo0HXzcrKwgMPPICoqCjI5XIsX7681uM2b96Mjh07wsnJCdHR0di2bVu15yVJwsqVKxEcHAxnZ2eMGDECFy9erHZMQUEBpk+fDg8PD3h5eWHevHlQq61/Q0HrK98OSiD0drNcSqtQIS7sdqztMQ6zegzHgNbBeBhZ+KjoDE6XXIZe4tJTRERUXalcg3eCTmHqvSlYt7wdro7uDZmLi+hY1EKcu3eHTC6mTnXp0gVZWVmmXwcOHDA999hjj+GXX37B5s2bsXfvXmRmZuK+++6rcY65c+diypQpDb6mRqOBv78/nn/+eXTv3r3WYw4dOoRp06Zh3rx5OHHiBCZMmIAJEybg7NmzpmPefPNNvPvuu9iwYQNiYmLg6uqK0aNHo7Ky0nTM9OnTce7cOezcuRNbt27Fvn378NBDDzU4q6WyvmknALD7VWDvG81+Wp3cEWdadUWsTzDiZFqcUqdCY+DdkURE1DS+RhcsyOyA2w7nAUkpouNQM/JbvBj+jy4x+3VXrVqFH3/8ESdPnqzxXHFxMfz9/fH1119j0qRJAIDz58+jU6dOOHz4MO64444Gn6suQ4cORY8ePfDOO+9Ue3zKlCkoKyvD1q1bTY/dcccd6NGjBzZs2ABJkhASEoInnngCK1asMGUODAzExo0bMXXqVCQkJKBz586Ii4tD7969AQA7duzA2LFjkZ6ejpCQkEZltSTWN/INAG37N8tpDDIFTod2x0fdx+LhniMxoF0EZjlcxfslZxFbfIHFm4iImsVVeTleDz2Byfen49OlUSi58zbIVCrRsagZON/WU9i1L168iJCQEERERGD69OlIS0sDABw7dgw6nQ4jRowwHduxY0e0adMGhw8fbvFchw8frnZtABg9erTp2snJycjOzq52jKenJ/r27Ws65vDhw/Dy8jIVbwAYMWIE5HI5YmJiWvw9tCTrW+0EAEL7AHJHwKhr1MuMMjnOB3dCnF9rxCokHC9Lg1pXCJRwORIiIjKP7a6Xsb0PENzLHQ+ld0PXA5mQ0jJEx6JboVDAuXsPIZfu27cvNm7ciA4dOiArKwsvvfQSBg0ahLNnzyI7OxtKpRJeXl7VXhMYGIjs7OwWz5adnY3AwMCbXvvaf+s7JiAgoNrzDg4O8PHxMct7aEnWWb6VLkBIDyA9rs7DJMhwMbADYgPaItZRhmNlV1CiLQVK482Tk4iI6CayFKV4qe0JoC0wsaQL7j7tCNfDZ7mVvRVRdYgStqvlXXfdZfr/bt26oW/fvmjbti2+/fZbODs7N8s13NzcTP//4IMPYsOGDc1yXntnneUbqJp6Ukv5vhzQHrEBEYhVKnC0LB2F2mJAnSAgIBERUcN875GI7wcCEXd4Y35qOCL3p0LKyqn/hSSUa9876j/ITLy8vBAVFYWkpCSMHDkSWq0WRUVF1Ua/c3JyEBQU1OBzXj8H3MPDo8GvCwoKqrGyyvXXvvbfnJwcBAcHVzvm2qorQUFByM3NrXYOvV6PgoKCRr0HS2Sdc74BoO0AAECaXzi+6zIST902FsM79cC9rlq8UnYeOwvPVRVvIiIiK3HZoRD/bHccU2cX4JdHoqG5IxoQtJIG1c+1fz/REUzUajUuXbqE4OBg9OrVC46Ojti1a5fp+cTERKSlpaFfv4Znbt++venXjVNA6tKvX79q1waAnTt3mq4dHh6OoKCgaseUlJQgJibGdEy/fv1QVFSEY8eOmY75888/YTQa0bdv3wZnsURWO/Kta9sf47vcjozyHKA8ESgXnYiIiKh5GCDhC+8EfDEM6DwwEHMut0b4vksw5l8VHY3+IlMq4XLdzYDmtmLFCowfPx5t27ZFZmYmXnzxRSgUCkybNg2enp6YN28eHn/8cfj4+MDDwwOPPvoo+vXrV22lk6SkJKjVamRnZ6OiosI00t25c2colcqbXvvacWq1Gnl5eTh58iSUSiU6d+4MAFi2bBmGDBmC1atXY9y4cdi0aROOHj2KDz74AAAgk8mwfPlyvPzyy4iMjER4eDheeOEFhISEYMKECQCATp06YcyYMViwYAE2bNgAnU6HJUuWYOrUqVa90glgrUsN/uWBXx/AmfwzomMQERG1OKWkwOyrnTDkmAaOJ84D1vvPt01w6dMHbT//TNj1p06din379uHq1avw9/fHwIED8corr6Bdu3YAqjbZeeKJJ/C///0PGo0Go0ePxrp166pN2Rg6dCj27t1b49zJyckICwu76bVlMlmNx9q2bYuUlBTTx5s3b8bzzz+PlJQUREZG4s0338TYsWNNz0uShBdffBEffPABioqKMHDgQKxbtw5RUVGmYwoKCrBkyRL88ssvkMvlmDhxIt59991qc9GtkVWX7zXH1+CjMx+JjkFERGRWvbTBmHkhCCH7L0Aq4hRLEfyXL4PfI4+IjkFWyKonkvUNtu45P0RERLfimDILy7qewJxHdDgw9zYYu0bV/yJqVq6NmDtNdD2rHvnWGDQY8L8B3AyHiIjs3sDK1ph23hcB+xMgqctEx7Fpcg8PRB0+BJlCIToKWSGrHvlWKVToEdBDdAwiIiLhDjhdweIeJ/HQYjniZvYGosJFR7JZLn1uZ/GmW2bV5RsABrUaJDoCERGRxSiUV+DfrU5i8sQr+GhpJIpG9ILMyUl0LJvi2r+/6Ahkxax62gkAJBcn454f7xEdg4iIyGIFGF3xcHoUuh3MhpRyRXQcqxexfRtU4fzJAt0aqy/fAHD3lruRWpIqOgYREZHFm1AaiXvOqOB26Byg04mOY3UcgoMRuftP0THIiln9tBMAGBw6WHQEIiIiq/Cj+0XM7X8WKx7zROLU2yFrFVz/i8jEbTA7BzWNTZTvIaFDREcgIiKyKmmKIrwQfgKTZ+Rhy6JoVPTrBvAmwnq5j7hTdASycjYx7URn1GHIpiEo1ZWKjkJERGS1IvW+mH+5LSL2X4aUmy86jsWRu7kh6tBByOrYep2oPjYx8u0od0S/EC52T0RE1BQXHa7i6ajjmDa3BDse6gFd7y5ALVuJ2yu3wYNYvKnJbKJ8A8CQ1px6QkRE1Bz0MiM+8T2L6SMT8fLjIci493bIvL1ExxLObTinnFDT2cS0EwAorCzE0G+HwigZRUchIiKyOU6SA+bmd8ag2DIoTieKjmN2MkdHRB4+BIWbm+goZOVsZuTb28kb0X7RomMQERHZpEqZHuv8T2PauEt4+7Ew5I67HTJ3+ymiLn36sHhTs7CZ8g0Aw9sMFx2BiIjI5h12SseSbicwfzFwZHYvSJ3aiY7U4rjKCTUXm5l2AgAZ6gyM+X6M6BhERER2586KMEyO94TP/nhIFRWi4zQvmQzt9+yBY2CA6CRkA2yqfAPA9F+n43T+adExiIiI7JKf0RULMiLR81AecNk2dp92io5G+OZvRccgG2FT004AYHTYaNERiIiI7Fa+vAyvtT6JyVMy8PmjHVAy7DarX57P/U5OOaHmY3Mj39ll2Rj13ShIsKm3RUREZLVaGTzwUFp7dD6YDulKpug4jRaxbRtUEeGiY5CNsLnyDQCzts/C8dzjomMQERHRdWQSMKm0A8adcoDLkXOAXi86Ur2cunZF+HebRccgG2Jz004ATj0hIiKyRJIM2OyRiNmDzuHZx3xw6f4+kFn4TYye4+8WHYFsjE2OfOdX5GPE5hEwSAbRUYiIiKgODpIc04s6YsRxI1RH4wGjBW2Wp1Agcu8eOPj5iU5CNsQmyzcAzP9tPmKyY0THICIiogbqqgvEnKRWaLM/CdLVAtFx4DpwINp89KHoGGRjbHLaCQCMDufUEyIiImty1jEHT3Q6jgcXlGHX/J7Q9+wkNI/nPeOFXp9sk82OfBdVFmHYt8Oglyz/Zg4iIiKq3e2aEMy4EIjgfYmQSkrMdl2ZiwuiDuyH3MXFbNck+2CzI99eTl4YFDpIdAwiIiJqgjhVJpZGn8CcRXocmtMLxi6RZrmu+513snhTi7DZ8g0A90XeJzoCERERNQO1TIt3gk5h6j3JeH95O1wd0xsyV9cWux5XOaGWYrPTTgDAYDRg5HcjkVeRJzoKERERNTMfgzMWZHdAr8P5wMWUZjuvwtcXkfv2QqZQNNs5ia6x6ZFvhVyB8e14swQREZEtKlBU4I1WJzF5Ujo+XdoBxXfeBpmTU5PP6zF2LIs3tRibHvkGgJTiFIz/kQWciIjIHgQZ3PBQeiSiD2ZBSk2/pXOEffcdnLt2aeZkRFVsvnwD3G6eiIjIHt1XGoXxp5VwPXS2wVvZqzp3QsQPP7RwMrJnNj3t5JoJ7SeIjkBERERm9oP7BcwZcBZPPeaFi5P7QBYSVO9rvCdPNkMysmd2MfJdrivH8M3DUaYrEx2FiIiIBFFAhqlFHTH6BOAUFw8YDNWel7m4IHLfPijcWm4VFSK7GPl2cXTB6DDueElERGTPDJDwlVcCZg5LwMrH/JE6sQ/kfr6m5z3G3sXiTS3OLka+AeBk7knM2D5DdAwiIiKyIEpJgVlXO2HocS0in34Rzt26iY5ENs5uyjcATPx5Ii4UXhAdg4iIiCxMJ59O+Hb8t6JjkB2wi2kn1zzQ8QHREYiIiMgCTekwRXQEshN2Vb7HRYyDl8pLdAwiIiKyIO5Kd4yNGCs6BtkJuyrfTg5OmBg5UXQMIiIisiD3trsXzg7OomOQnbCr8g0AUztOhYPMQXQMIiIisgAyyDjlhMzK7sp3kGsQhrUZJjoGERERWYABrQYgzDNMdAyyI3ZXvgFgeqfpoiMQERGRBZjdZbboCGRn7LJ89wrshU4+nUTHICIiIoE6+XRC3+C+omOQnbHL8g0AD3TisoNERET2bFaXWaIjkB2y2/I9NnwsfJx8RMcgIiIiAYJdgzE6bLToGGSH7LZ8KxVKTO0wVXQMIiIiEmB6p+lwkHP1MzI/uy3fQNXUExcHF9ExiIiIyIzcHd0xKWqS6Bhkp+y6fHuqPHF/1P2iYxAREZEZTYqaBFdHV9ExyE7ZdfkGqm62UMqVomMQERGRGTjIHbjkMAll9+Xb38UfE9pPEB2DiIiIzGBs+FgEugaKjkF2zO7LNwDM6TqHW84TERHZOLlMjjld5oiOQXaO5RtAqHso7gq/S3QMIiIiakEj245Ee+/2omOQnWP5/sv86PmQQSY6BhEREbUAuUyOhd0Xio5BxPJ9TYRXBIa3GS46BhEREbWAUW1HoZ1XO9ExiFi+r7eg2wLREYiIiKiZcdSbLAnL93W6+HbB4NDBomMQERFRMxrddjQivCJExyACwPJdw9KeSzn3m4iIyEbIZXI80v0R0TGITFi+b9DBpwPGhI0RHYOIiIiawegwjnqTZWH5rsWSnku47jcREZGV46g3WSKW71q08WiDCZETRMcgIiKiJhgTNgYRnhz1JsvC8n0Tj3R7BCqFSnQMIiIiugUOcgcs7rFYdAyiGli+byLQNRBTO0wVHYOIiIhuweSoyWjj0UZ0DKIaWL7rMD96Ptwc3UTHICIiokZwd3Tnut5ksVi+6+Dl5IWZnWeKjkFERESNMC96HrycvETHIKoVy3c9ZnaZCR8nH9ExiIiIqAGCXYPxYOcHRccguimW73q4OrpiUfdFomMQERFRAzza81EumEAWjeW7ASZFTUKUd5ToGERERFSHTj6dcHfE3aJjENWJ5bsBFHIFnr79adExiIiIqA4req+ATCYTHYOoTizfDdQnuA/ubHOn6BhERERUi8Ghg9EnuI/oGET1YvluhBW9V0ApV4qOQURERNdRyBR4vNfjomMQNQjLdyOEuodiZhcuPUhERGRJpnWchnZe7UTHIGoQlu9GWhC9AAHOAaJjEBEREQB/Z39uI09WheW7kVwcXbCs1zLRMYiIiAjAE72fgJuSu1GT9WD5vgXjI8Yj2i9adAwiIiK71ieoD8ZFjBMdg6hRWL5vgUwmw7N9noVcxt8+IiIiERzkDniu73OiYxA1GtvjLYr2j8aUDlNExyAiIrJLMzrPQIRXhOgYRI3G8t0Ey25bhkCXQNExiIiI7EqgSyAe6faI6BhEt4TluwlcHV3xbN9nRccgIiKyK0/d/hRcHF1ExyC6JSzfTXRnmzsxvPVw0TGIiIjsQv+Q/hgVNkp0DKJbxvLdDP7Z959wdXQVHYOIiMimOSmceJMlWT2W72YQ6BqIpT2Xio5BRERk0x7t+SjaeLQRHYOoSVi+m8nUjlPRza+b6BhEREQ2qbt/dzzY+UHRMYiajOW7mchlcrzY/0U4yBxERyEiIrIpKoUK/zfg/7i/BtkEfhY3oyjvKMzuOlt0DCIiIpvySPdHEOHJNb3JNrB8N7OF3ReivVd70TH+v707D4+6PvA4/pl7cpGQQO4AISGJhkMuOUQIgqBrgdhqBapiq3bVbpWtSLVq0RWqkgqCx1ofL9SH9ZGu13K0VbFSF2ShHAqCgBCukIQAuUlmkpn9g262qaAgyXzneL+eJw8kM5l5wyP4eYbf/H4AAISFwqRC/bjwx6YzgA7D+O5gTptTj176qOxWDj8BAOB8OKwOPXLJI7JZbaZTgA7D+O4EBYkFun3A7aYzAAAIabf2v1V9uvYxnQF0KMZ3J7m5783q352znwAA8F3kd83XLf1uMZ0BdDjGdyexWW2ad8k8RdmjTKcAABBSHFaH5o6aK4fVYToF6HCM707UK76XZg6aaToDAICQctegu1SQWGA6A+gUjO9ONq1gmoanDTedAQBASBiRNkI3Xnij6Qyg01j8fr/fdES4K28o1/ff+77qPHWmUwAACFpdXV31n5P/U92ju5tOAToN4ztA/uur/9KvPvmV6QwgaBxbfUzHVx+Xt8orSXJluJQ8JVlx/eMkSc2VzSp/o1yNuxvl9/oV2y9W6denyx5/5tN4tp5sVeVblardVKuW2ha5e7qVNj1N0b2j2+5TtapKR1celSR1/6fu6nZlt7bbGr9qVNmrZcr5dY4sNktn/LIBfIOnLntKRVlFpjOATsVhJwEyKWeSJudMNp0BBA1HV4dSr01VzkM5ynkoR7EXxOrAogNqOtwkX7NPpSWlslgsyp6drd7395a/xa/9T+6X33fm1wsOv3xY9dvrlfnTTOXOzVVsYaxKS0rlPXFq4DcdbFLF2xXKuj1LWbdnqeKtCjUdbJIk+Vv9KltSpvQZ6QxvwIDr8q9jeCMiML4D6P5h9ys7Ptt0BhAUugzsorgBcXKluuRKdSnlmhRZ3VY17mlUw+4Geau8yrglQ+4st9xZbmXemqmTpSfVsKPhtI/n8/hUu7FWqT9MVUx+jFwpLqVcnSJnslPHVx+XJDUfaZY7063YC2MVe2Gs3FluNR9plnTqFfGY/Jh2r5IDCIzchFzNGjLLdAYQEIzvAIp2ROu3Y34rt81tOgUIKn6fX9WfVsvX7FN0brT8Xr9kkSz2/38F2uKwSBapYdfpx7e/1S/5JIuz/avWVqe17XtcmS55KjzyHPPIU+VRc3mzXJkuNVc268RfTij5+8md94sEcFpOq1OPj35cbjv/b0Rk4BroAZbXNU+/vPiXenjdw6ZTAOOaDjZp79y98nl9srqs6vHzHnJnuGWPs8vqsqrizQqlXJMiSSp/s1zySS01Lad9LFuUTVG5Uap8t1KuNJfs8XbVfFqjxj2NcqY4JUnudLdSfpCi0pJSSVLqNalyp7u1b/4+pf4wVfXb6lX5TqUsNovSfpSmmPyYgPw+AJHsXwf/q/K65pnOAAKGN1waMnvNbK3at8p0BmCUr8Un7zGvfCd9qtlQoxNrTij73my5M9yq21ansiVlp96QaZHih8WruaxZ0b2jlT4j/bSP11zZrMMvHlbjl42SVYrqGSVnqlNNpU3q8+jpL1F94pMTqt1Uq4wZGdp17y7lzMmR94RXh353SHklebI6+AdCoLMUZRbpqXFPmc4AAopXvg2ZM2KOvjj2hfbX7jedAhhjtVvlSnFJkqJ6RenkvpM69v4xZdyUobi+ccovyVdLXYssVotsMTbtvHOnHN3PfMU7V7JLve/rLV+zT60nW+VIcOjAswfO+D0tdS2qfLdSve/rrca9jW3Hn7tSXfK3+uUp98idxT+FA50hKy5L8y6dZzoDCDhe0jEkxhGj3475rZxWp+kUIHj4dep4779jj7PLFmNT/Rf1aqlrUZeBXb71YawuqxwJDrU2tKr+83p1GXT67zmy9Ii6TegmR6JD8v3tuPH/S2n1f+OZVQB8d26bWwuLFqqL89v/PAPhhvFtUEFige4Zeo/pDMCI8mXlaviyQZ6jHjUdbDr1+c4GJYxIkCSd+MsJNe5pVHNls6rXVuvgMweVNCFJrjRX22Pse3yfjn1wrO3zus/rVPdZnTxHParfVq99j+2TK82lrqO6fu3567fVy1PhUeK4RElSVHaUmo80q+6zOh3/83FZrJZ2zwWg4zww/AHlJ+abzgCM4LATw6YWTNXGio36Y+kfTacAAdVS26JDzx9SS02LrFFWubPc6nV3L8X2jZV06rSAFcsq1NrQKkc3h7pP6q6kiUntHsNT6VFL3f+/AdN30qfyZeVqOdEiW4xNXYZ0UcoPUtqdNUU6dVrCstfLlHV7lizWU7c5Eh1Kuz5Nh184LIvDosxbMmV18voE0NGuzbtWU3KnmM4AjOENl0Gg0duoG1bdoF0ndplOAQCg0/RN6qslVy6R08Yhl4hcvKwTBKId0Vo0dpESXAmmUwAA6BQJrgQtKFrA8EbEY3wHicy4TJWMKZHNYjOdAgBAh7JarHp89ONKi00znQIYx/gOIsPThuvuIXebzgAAoEPdMeAOjUwfaToDCAqM7yBzw4U3aHLOZNMZAAB0iMt7Xq6f9v+p6QwgaPCGyyDU3Nqsm1bdpG3HtplOAQDgOytMKtTLV7ysKHuU6RQgaPDKdxBy2VxaOHahktxJ335nAACCUHJ0shZftpjhDfwDxneQSo1J1cKxC+WwnvlS2gAABKMoe5SevuxpJUcnm04Bgg7jO4gNTB6oOSPmmM4AAOCsWWTRo6Me1QVJF5hOAYIS4zvITcmdotsH3G46AwCAs3LnoDs1ruc40xlA0GJ8h4A7LrpDU3K4FC8AILhNzpmsW/rdYjoDCGqc7SREeH1e/eyDn2ndkXWmUwAA+JpByYP0woQX5LDxXiXgmzC+Q0i9p14z/jBDu07sMp0CAECbnl166rUrX1NXd1fTKUDQ47CTEBLrjNUz457h3eMAgKDRPaq7fnf57xjewFlifIeY1JhUPTvuWcU6Yk2nAAAiXJwzTs9d/pwyYjNMpwAhg/EdgvIT8/VE0ROyW+2mUwAAEcplc+mpy55SXtc80ylASGF8h6iR6SP18MiHZZHFdAoAIMLYLDaVjC7R4JTBplOAkMP4DmGTcybr3ovvNZ0BAIgwc0bM0dgeY01nACGJ8R3ipl8wXXcOvNN0BgAgQtw16C5d3edq0xlAyGJ8h4Fb+9+qm/vebDoDABDmrr/gei6iA5wnxneYmDl4pq7Lv850BgAgTE3qPUmzh842nQGEPMZ3GLl/2P2anDPZdAYAIMxcmX2lHrnkEVksvMkfOF9c4TLMtPpaNevjWfrgwAemUwAAYWBCzwmaP3q+bFab6RQgLDC+w5C31aufr/65/rvsv02nAABC2Pge41UypoTrSgAdiPEdpppamnTHh3doQ/kG0ykAgBBUlFWkBUUL5LA6TKcAYYVjvsOU2+7Ws+Oe1Yi0EaZTAAAhZnTmaC0Yw/AGOgPjO4y57W49Ne4pjcoYZToFABAiLsm4RAuLFsphY3gDnYHxHeZcNpcWj12soqwi0ykAgCA3Im2EFo1dJKfNaToFCFuM7wjgsDm0oGiBxvcYbzoFABCkRqaP1OLLFstlc5lOAcIa4ztCOKwOlYwp0RW9rjCdAgAIMpf3vFxPX/a03Ha36RQg7DG+I4jdatdjlz6m7/X+nukUAECQKM4tVsnoEo7xBgKEUw1GIJ/fpzlr5+idPe+YTgEAGHT9Bddr9tDZXLkSCCDGd4Ty+/2av2G+Xt/xuukUAIABtw24TT+76GemM4CIw/iOcC98/oIWbVpkOgMAECAWWTRryCzdWHij6RQgIjG+oXf3vKuH1j6kFn+L6RQAQCeyWWyaM2KOru5ztekUIGIxviFJWnNojWZ9PEsnW06aTgEAdAKH1aHHLn1ME3pNMJ0CRDTGN9psPbpV//Lhv6i6udp0CgCgA8U54/Rk0ZO6OO1i0ylAxGN8o529NXt12/u36UjDEdMpAIAOkB6TrmfHP6uchBzTKQDE+MZpVDRU6LYPbtOe6j2mUwAA56EwqVBPj3ta3aK6mU4B8DdcZAdfkxKToiVXLtGQlCGmUwAA31FRVpFevuJlhjcQZBjfOK0uzi56/vLnNSVniukUAMA5ml4wXYvGLlKUPcp0CoB/wGEn+FYvfv6iFm1aJL/4TwUAgpnVYtXdg+/mHN5AEGN846x8uP9D3ffJfZyKEACClNvm1mOXPqZxPceZTgHwDRjfOGtfHPtCd310l8obyk2nAAD+Tkp0ihaNXaTCboWmUwB8C8Y3zknVySrN/Gimth7dajoFACBpUPIgLShaoKSoJNMpAM4C4xvnzNPq0cPrHtZ7X71nOgUAItrU/KmaffFsOawO0ykAzhLjG9/Zku1LtPCvC9XqbzWdAgARxWl16oHhD+jqPlebTgFwjhjfOC8byjdo9prZqjpZZToFACJCcnSynix6Uv269zOdAuA7YHzjvFWdrNKsj2fprxV/NZ0CAGFtYPJALShawIVzgBDG+EaHaPW1atHmRXp528umUwAgLF2Xf51+efEvOb4bCHGMb3So1QdW64FPHlCdt850CgCEhWh7tB4Y/oAm5UwynQKgAzC+0eEO1h7ULz7+hXYe32k6BQBCWkFigUpGl6hXfC/TKQA6COMbnaK5tVnzPp2nt/e8bToFAELS1PypumfoPXLanKZTAHQgxjc61du739aj//Mol6UHgLMU54zTv438N43vOd50CoBOwPhGp9tfu1/3rrlX245tM50CAEGtf/f+KhldovTYdNMpADoJ4xsB0eJr0bNbntWL216Uz+8znQMAQcUii27qe5PuHHin7Fa76RwAnYjxjYDaVLFJv/rkVzpcf9h0CgAEhSR3kuaOmqtRGaNMpwAIAMY3Aq7eU6+56+dqxd4VplMAwKgJPSfoweEPKsGdYDoFQIAwvmHMyr0rNffTuZwTHEDEiXfF6/5h9+vK7CtNpwAIMMY3jDpSf0T3fXIfl6YHEDHGZI7RQyMf4hLxQIRifMM4v9+v/9j5H1q0aZEaWxpN5wBAp4h1xGr20Nm6us/VplMAGMT4RtAoqy/Tw+se1tqytaZTAKBDDUsdpkcueURpsWmmUwAYxvhG0Hlnzzsq2VCiWk+t6RQAOC9R9ijNHDRT0wqmyWKxmM4BEAQY3whKVSerNPfTufrwwIemUwDgO7kk4xI9OPxBZcRmmE4BEEQY3whqfyr9k36z/jc61nTMdAoAnJVEd6JmD52tq3pfZToFQBBifCPo1TTXaP6G+Xrvq/dMpwDANyrOLdasIbMU74o3nQIgSDG+ETI2lG/Qb9b/Rnuq95hOAYB2cuJz9MDwBzQkdYjpFABBjvGNkNLia9HSHUv171v/XfXeetM5ACJclD1K/9z/n3Vj4Y1yWB2mcwCEAMY3QlLVySo9sfEJLd+73HQKgAhVlFWk+y6+T+mx6aZTAIQQxjdC2qaKTZq3fp52ndhlOgVAhMhNyNU9Q+/RyPSRplMAhCDGN0Jeq69Vb3z5hp7Z/IzqvHWmcwCEqa6urrrjojt0bd61slltpnMAhCjGN8LGsZPHtHjzYr275121+ltN5wAIE3arXdMKpum2Abepi7OL6RwAIY7xjbCz58QeLdy0UGsOrTGdAiDEFWUWadbQWerZpafpFABhgvGNsLWhfIMW/nWhPq/63HQKgBCTm5Cr2UNna0T6CNMpAMIM4xth7w+lf9DiTYt1sO6g6RQAQS41JlW39b9NxbnFHNcNoFMwvhERvD6v3vzyTT3/2fM63nTcdA6AIJPkTtKt/W/VtXnXymlzms4BEMYY34goDd4GvbTtJb32xWs62XLSdA4Aw+Jd8bqp8Cb96IIfKcoeZToHQARgfCMiHW86riXbl+iNnW+osaXRdA6AAItxxOiGC2/QjRfeqDhnnOkcABGE8Y2IVt1UrVe/eFVLdy5Vg7fBdA6ATua2uTW1YKp+0vcn6uruajoHQARifAOSapprTo3wHUtV7603nQOgg0XZo/SDPj/Qj/v+WMnRyaZzAEQwxjfwd2o9tXr9i9f1+hevc7VMIAzEu+I1vWC6phdMV4I7wXQOADC+gdOp89Tp9R2va+mOpapurjadA+AcpUSn6MYLb9Q1edco2hFtOgcA2jC+gW/Q1NKk9756T6998ZpKa0tN5wD4Fr269NJP+v5E3+v9PTlsDtM5APA1jG/gLPj9fn186GMt2b5EGys2ms4B8A8Kkwp1c7+bNa7HOFktVtM5AHBGjG/gHG0/tl2vbn9Vfyr9k1r8LaZzgIhlt9g1tsdYTS+YriGpQ0znAMBZYXwD31F5Q7mW7liq3+/+veo8vDkTCJREd6KuybtGP8z7oVJiUkznAMA5YXwD56nR26jle5fr97t+rx3Hd5jOAcJWv279NK1gmq7odQXHcwMIWYxvoANtq9qmZbuWadW+VVy+HugATqtTE3tN1LSCaerXvZ/pHAA4b4xvoBPUe+q1fO9yLdu1TLtO7DKdA4Sc7PhsFecWa0rOFCVFJZnOAYAOw/gGOtlnRz/Tsl3L9MfSP/JqOPANYh2xuiL7ChXnFmtA9wGmcwCgUzC+gQCp89Rp1b5VWrF3hTZXbpZf/NEDLLJoWNowTcmdovE9xsttd5tOAoBOxfgGDCirL9PKfSu1/Kvl+qrmK9M5QMBlxmZqSu4UTcmZorTYNNM5ABAwjG/AsJ3Hd2rF3hVauW+lKhsrTecAnaZbVDeN7zFeV2RfoUHJg2SxWEwnAUDAMb6BIOHz+7ShfINW7F2hD/Z/oDov5w5H6EtyJ2l8z/Ga2GuiBqcM5uqTACIe4xsIQt5Wrz498qlWH1ytPx/8s6pOVplOAs4agxsAzozxDQQ5n9+nz45+ptUHVmv1wdXaX7vfdBLwNcnRyRqbNVYTek7QkNQhDG4AOAPGNxBi9pzYo9UHV2v1gdXafmy76RxEKJvFpn7d+ml05miNzhyt/MR800kAEBIY30AIK28o118O/0XrytZp/ZH1qvXUmk5CGEtwJeiSjEt0acalGpUxSvGueNNJABByGN9AmPD5fdpetV3rjqzT2rK12np0q1p8LaazEMKsFqvyu+ZrVMYojc4crf7d+3M4CQCcJ8Y3EKYavY3aWLFRa8vWal3ZOu2t2Ws6CUHu/8b2kNQhGpoyVINTB6uLs4vpLAAIK4xvIEJUNFRoc+Xmto9dJ3ap1d9qOgsGMbYBIPAY30CEavQ26vOqz7W5crO2VG7R1qNbVe+tN52FThTriNWFSReqsFuhBnYfyNgGAAMY3wAknTpmfPeJ3dp6dKu2Ht2qHcd3aF/1PrX4OW48FDmtTuUn5qswqVD9uvdT36S+6hXfi2O2AcAwxjeAM/K0erS7ere+PP6ldh7fqZ3Hd2rXiV1q8DaYTsPfcdvcyo7PVl7XPPXt1lf9uvVTXtc8OWwO02kAgH/A+AZwTvx+vw7WHWwb47urd6u0plSH6g9xdpVO5rQ6lR2frZyEHOUm5Lb9mBmXySvaABAiGN8AOkSrr1WH6w+rtLZU+2v3a3/t/rafVzRUyC/+qjkbNotNKdEpSo9NV0ZshrListqGdlZclmxWm+lEAMB5YHwD6HRNLU3aX7tfh+oPqaKhQpWNlaporDj18bfPm1qbTGcGhM1iU6I7sW1ct33EnfoxNSZVDiuHiwBAuGJ8AwgKNc01Km8oV0XjqTF+9ORR1TTXqLq5WtXN1appOvXzWk+t6r318vl9ppMlnRrT0fZoxTpjlehOVKI7UUlRSUpyJykpKulrnye4EjhEBAAiGOMbQMjx+/2q99arzlOnOk+dGrwNam5tltfnlafVo+bWZnlaPac+fJ52P2/xtchischmsckii2xWm6yyymqxymb929csNlksFtmtdkXboxXtiFaMI0Yxjpi2z6Ptp77mtrtN/3YAAEII4xsAAAAIEP7tEwAAAAgQxjcAAAAQIIxvAAAAIEAY3wAAAECAML4BAACAAGF8AwAAAAHC+AYAAAAChPENAAAABAjjGwAAAAgQxjcAAAAQIIxvAAAAIEAY3wAAAECAML4BAACAAGF8AwAAAAHC+AYAAAAChPENAAAABAjjGwAAAAgQxjcAAAAQIIxvAAAAIEAY3wAAAECAML4BAACAAGF8A2FizZo1mjRpktLT02WxWPTOO++0u93v9+vXv/610tLSFBUVpfHjx2v37t3t7jNv3jyNHDlS0dHRSkhIOKvnLS0tlcVi+drHp59+2u5+y5YtU0FBgdxut/r166eVK1eecx8AAKGO8Q2EiYaGBg0YMEDPPPPMaW+fP3++Fi9erOeee07r169XTEyMJk6cqKamprb7eDweXXvttbr99tvP+fk/+OADHTlypO1j8ODBbbetXbtW06ZN080336zNmzeruLhYxcXF2rZt2zn1AQAQ6ix+v99vOgJAx7JYLHr77bdVXFws6dSryunp6br77rs1a9YsSVJNTY1SUlL0yiuvaOrUqe2+/5VXXtHMmTNVXV39rc9VWlqq7Oxsbd68WRdddNFp73PdddepoaFBy5cvb/va8OHDddFFF+m555475z4AAEIVr3wDEWDfvn0qLy/X+PHj274WHx+vYcOGad26dR3yHJMnT1ZycrJGjRql9957r91t69ata/fckjRx4sS25w5EHwAAwYDxDUSA8vJySVJKSkq7r6ekpLTd9l3FxsbqiSee0LJly7RixQqNGjVKxcXF7QZ4eXn5Nz53Z/YBABBM7KYDAISOwsJC7d+/X5J06aWXatWqVerWrZt+8YtftN1n6NChKisrU0lJiSZPnmwqFQCAoMT4BiJAamqqJKmiokJpaWltX6+oqDjjcdqns3LlSnm9XklSVFTUGe83bNgwvf/+++2ev6Kiot19Kioq2ro6qg8AgGDHYSdABMjOzlZqaqo+/PDDtq/V1tZq/fr1GjFixFk/Ts+ePZWbm6vc3FxlZGSc8X5btmxpN6JHjBjR7rkl6f3332977o7qAwAg2PHKNxAm6uvrtWfPnrbP9+3bpy1btigxMVE9evTQzJkzNXfuXPXp00fZ2dl68MEHlZ6e3nZGFEk6cOCAjh8/rgMHDqi1tVVbtmyRJOXm5io2Nva0z7tkyRI5nU4NHDhQkvTWW2/ppZde0gsvvNB2n7vuuktjxozRE088oauuukpvvPGGNm7cqOeff17SqbOznE0fAAAhzw8gLHz00Ud+SV/7mDFjht/v9/t9Pp//wQcf9KekpPhdLpd/3Lhx/i+//LLdY8yYMeO0j/HRRx+d8XlfeeUV/wUXXOCPjo72d+nSxX/xxRf7ly1b9rX7vfnmm/68vDy/0+n0FxYW+lesWNHu9rPpAwAg1HGebwAAACBAOOYbAAAACBDGNwAAABAgjG8AAAAgQBjfAAAAQIAwvgEAAIAAYXwDAAAAAcL4BgAAAAKE8Q0AAAAECOMbAAAACBDGNwAAABAgjG8AAAAgQP4Xcw5gv2r3GcUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "less than 10 : 14\n",
            "10-100 : 58\n",
            "101-500 : 67\n",
            "501-1000 : 9\n",
            "more than 1000 : 20\n",
            "Максимальное значение сложности: 150174\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Надежность**\n",
        "\n",
        "* Незначительная ошибка\n",
        "\n",
        "1.  Не влияет на основные функции приложения\n",
        "2.  Не приводит к потере данных или некорректному отображению информации\n",
        "3.  Не создает серьезных неудобств для пользователей\n",
        "4.  Может быть исправлена в ближайшем релизе без срочной необходимости\n",
        "\n",
        "* Серьезная ошибка\n",
        "1.  Нарушает основные функции приложения, но не делает его полностью неработоспособным\n",
        "2.  Может приводить к некорректному отображению данных или незначительной потере информации\n",
        "3.  Создает серьезные неудобства для пользователей, но не делает приложение неиспользуемым\n",
        "4.  Требует исправления в ближайшем релизе, но не является критической для выпуска\n",
        "* Критическая ошибка\n",
        "1.  Делает приложение полностью или частично неработоспособным\n",
        "2.  Приводит к потере важных данных или некорректному отображению критической информации\n",
        "3.  Делает приложение практически неиспользуемым для пользователей\n",
        "4.  Требует срочного исправления и выпуска патча, так как критична для работы приложения\n",
        "* Блокирующая ошибка\n",
        "1.  Делает приложение полностью неработоспособным и неиспользуемым\n",
        "2.  Приводит к серьезной потере данных или делает их недоступными\n",
        "3.  Не позволяет пользователям выполнять какие-либо действия в приложении\n",
        "4.  Требует немедленного исправления, так как блокирует всю работу с приложением"
      ],
      "metadata": {
        "id": "k0HvZHHo8z8p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reliability_ranges = {\n",
        "    '0 ошибок': 0.0,\n",
        "    'как минимум 1 незначительная ошибка': 0.0,\n",
        "    'как минимум 1 серьезная ошибка': 0.0,\n",
        "    'как минимум 1 критическая ошибка': 0.0,\n",
        "    'как минимум 1 блокирующая ошибка':0.0\n",
        "}\n",
        "\n",
        "for index, project in merged_df.iterrows():\n",
        "    if project['metric'] == 'reliability_rating':\n",
        "        reliability_count = float(project['value'])\n",
        "        if reliability_count == 1.0:\n",
        "            reliability_ranges['0 ошибок'] += 1\n",
        "        elif reliability_count == 2.0:\n",
        "            reliability_ranges['как минимум 1 незначительная ошибка'] += 1\n",
        "        elif reliability_count == 3.0:\n",
        "            reliability_ranges['как минимум 1 серьезная ошибка'] += 1\n",
        "        elif reliability_count == 4.0:\n",
        "            reliability_ranges['как минимум 1 критическая ошибка'] += 1\n",
        "        elif reliability_count == 5.0:\n",
        "            reliability_ranges['как минимум 1 блокирующая ошибка'] += 1\n",
        "\n",
        "for key, value in reliability_ranges.items():\n",
        "    print(f\"{key} : {int(value)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7w0ntQ28u2nz",
        "outputId": "f0f2fbac-63b8-4a34-ce2b-62f8183ba420"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 ошибок : 98\n",
            "как минимум 1 незначительная ошибка : 4\n",
            "как минимум 1 серьезная ошибка : 55\n",
            "как минимум 1 критическая ошибка : 13\n",
            "как минимум 1 блокирующая ошибка : 27\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categories = ['A', 'B', 'C', 'D', 'E']\n",
        "values = [int(reliability_ranges['0 ошибок']),\n",
        "          int(reliability_ranges['как минимум 1 незначительная ошибка']),\n",
        "          int(reliability_ranges['как минимум 1 серьезная ошибка']),\n",
        "          int(reliability_ranges['как минимум 1 критическая ошибка']),\n",
        "          int(reliability_ranges['как минимум 1 блокирующая ошибка'])]\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(categories, values, color='skyblue')\n",
        "plt.xlabel('Категории')\n",
        "plt.ylabel('Количество проектов')\n",
        "plt.title('Распределение проектов по категориям надежности')\n",
        "for i, value in enumerate(values):\n",
        "    plt.text(i, value + 0.5, str(value), ha='center', va='bottom')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "4FKogRaWU6rP",
        "outputId": "b2e29883-f9eb-4e15-edc0-5eb017a1654a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Безопасность**\n",
        "* Незначительная уязвимость\n",
        "1. Не позволяет злоумышленнику получить доступ к конфиденциальным данным или выполнить вредоносный код\n",
        "2. Не создает серьезных рисков для безопасности системы\n",
        "3. Может быть исправлена в ближайшем обновлении без срочной необходимости\n",
        "4. Обычно связана с незначительными ошибками в реализации или конфигурации\n",
        "* Серьезная уязвимость\n",
        "1. Может позволить злоумышленнику получить повышенные привилегии или доступ к чувствительной информации\n",
        "2. Создает значительные риски для безопасности системы, но не делает ее полностью уязвимой\n",
        "3. Требует исправления в ближайшем обновлении, но не является критической для безопасности\n",
        "4. Может быть связана с ошибками аутентификации, авторизации или обработки ввода\n",
        "* Критическая уязвимость\n",
        "1. Позволяет злоумышленнику получить полный контроль над системой или выполнить вредоносный код\n",
        "2. Создает серьезные риски для безопасности и может привести к компрометации всей системы\n",
        "3. Требует срочного исправления, так как критична для безопасности\n",
        "4. Может быть связана с ошибками в реализации криптографии, выполнении команд или управлении памятью\n",
        "* Уязвимость блокатор\n",
        "1. Делает систему полностью уязвимой и неспособной противостоять атакам\n",
        "2. Позволяет злоумышленнику получить полный контроль и выполнить любой вредоносный код\n",
        "3. Создает критические риски для безопасности и должна быть исправлена немедленно\n",
        "4. Может быть связана с фундаментальными ошибками в дизайне или реализации системы"
      ],
      "metadata": {
        "id": "xOgBUUowF6bb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "security_ranges = {\n",
        "    '0 уязвимостей': 0.0,\n",
        "    'не менее 1 незначительной уязвимости': 0.0,\n",
        "    'не менее 1 серьезной уязвимости': 0.0,\n",
        "    'не менее 1 критической уязвимости': 0.0,\n",
        "    'не менее 1 уязвимости блокатора':0.0\n",
        "}\n",
        "\n",
        "for index, project in merged_df.iterrows():\n",
        "    if project['metric'] == 'security_rating':\n",
        "        security_count = float(project['value'])\n",
        "        if security_count == 1.0:\n",
        "            security_ranges['0 уязвимостей'] += 1\n",
        "        elif security_count == 2.0:\n",
        "            security_ranges['не менее 1 незначительной уязвимости'] += 1\n",
        "        elif security_count == 3.0:\n",
        "            security_ranges['не менее 1 серьезной уязвимости'] += 1\n",
        "        elif security_count == 4.0:\n",
        "            security_ranges['не менее 1 критической уязвимости'] += 1\n",
        "        elif security_count == 5.0:\n",
        "            security_ranges['не менее 1 уязвимости блокатора'] += 1\n",
        "\n",
        "for key, value in security_ranges.items():\n",
        "    print(f\"{key} : {int(value)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CJ9wXfPH0Ogp",
        "outputId": "38d36d36-6cec-4f05-9bf4-288fe26a1dee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 уязвимостей : 149\n",
            "не менее 1 незначительной уязвимости : 0\n",
            "не менее 1 серьезной уязвимости : 3\n",
            "не менее 1 критической уязвимости : 1\n",
            "не менее 1 уязвимости блокатора : 44\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categories = ['A', 'B', 'C', 'D', 'E']\n",
        "values = [int(security_ranges['0 уязвимостей']),\n",
        "          int(security_ranges['не менее 1 незначительной уязвимости']),\n",
        "          int(security_ranges['не менее 1 серьезной уязвимости']),\n",
        "          int(security_ranges['не менее 1 критической уязвимости']),\n",
        "          int(security_ranges['не менее 1 уязвимости блокатора'])]\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(categories, values, color='skyblue')\n",
        "plt.xlabel('Категории')\n",
        "plt.ylabel('Количество проектов')\n",
        "plt.title('Распределение проектов по категориям безопасности')\n",
        "for i, value in enumerate(values):\n",
        "    plt.text(i, value + 0.5, str(value), ha='center', va='bottom')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Vb-vD-JQVVMJ",
        "outputId": "640c8af4-4441-4d39-d8e6-7a3327a25113"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "duplicated_ranges = {\n",
        "    'less than 1%': 0.0,\n",
        "    '1-10%': 0.0,\n",
        "    '11-50%': 0.0,\n",
        "    '51-80%': 0.0,\n",
        "    'more than 80%':0.0\n",
        "}\n",
        "\n",
        "for index, project in merged_df.iterrows():\n",
        "    if project['metric'] == 'duplicated_lines_density':\n",
        "        duplicates_count = float(project['value'])\n",
        "        if duplicates_count < 1:\n",
        "            duplicated_ranges['less than 1%'] += 1\n",
        "        elif 1 <= duplicates_count <= 10:\n",
        "            duplicated_ranges['1-10%'] += 1\n",
        "        elif 11 <= duplicates_count <= 50:\n",
        "            duplicated_ranges['11-50%'] += 1\n",
        "        elif 51 <= duplicates_count < 80:\n",
        "            duplicated_ranges['51-80%'] += 1\n",
        "        elif 80 <= duplicates_count :\n",
        "            duplicated_ranges['more than 80%'] += 1\n",
        "\n",
        "for key, value in duplicated_ranges.items():\n",
        "    print(f\"{key} : {int(value)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BpqZkqRu89Ov",
        "outputId": "7fa50dc5-6486-4085-a11e-08050eb7c585"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "less than 1% : 127\n",
            "1-10% : 51\n",
            "11-50% : 13\n",
            "51-80% : 2\n",
            "more than 80% : 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "bars = plt.bar(duplicated_ranges.keys(), duplicated_ranges.values())\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.xlabel('Диапазон дублированных строк')\n",
        "plt.ylabel('Количество проектов')\n",
        "plt.title('Распределение плотности дублированных строк в проектах')\n",
        "\n",
        "# Добавление количества проектов на столбцы\n",
        "for bar in bars:\n",
        "    height = bar.get_height()\n",
        "    plt.annotate(f\"{int(height)}\",\n",
        "                xy=(bar.get_x() + bar.get_width() / 2, height),\n",
        "                xytext=(0, 3),\n",
        "                textcoords=\"offset points\",\n",
        "                ha='center', va='bottom')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "rNnNIkGS_24n",
        "outputId": "a607c8c0-a9ce-4374-c8fe-478fb5772fcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}